Business Intelligence

What’s new with Google Data Cloud

Thu, 29 Oct 2026 16:00:00 +0000

October 26 - October 30

Knowledge Catalog introduces self-learning context enrichment and Knowledge Studio (Preview)
Knowledge Catalog's enrichment agent now learns from agent and user interactions and from sources such as playbooks, code repositories, and business documents to find and fix missing or outdated context, which teams can package into reusable context sets. Knowledge Studio, the new studio experience in Knowledge Catalog, lets context engineers and data stewards explore their data estate conversationally, navigate a visual context graph, and review changes before they reach users and agents. Based on internal benchmarks, grounding agents in Knowledge Catalog makes them nearly 4x more accurate, with up to 33% lower latency and using up to 75% fewer tokens. Read the launch blog.
Knowledge Catalog now grounds AI agents in the semantic models you already trust (GA)
Knowledge Catalog natively ingests semantic models and metric definitions from Looker LookML, BigQuery Graph, dbt MetricFlow, Snowflake Semantic Views, Databricks Metric Views, and SAP Core Schema Notation, and connects them with lineage, data quality, and access controls in a single context graph. Gemini Enterprise, BigQuery conversational analytics, and custom agents retrieve a metric's trusted formula, verified join path, and backing table in one context call, without moving data or rewriting business logic. Read the launch blog.

October 19 - October 23

BigQuery introduces column aliases in GoogleSQL and cross-region table copy GA
GoogleSQL in BigQuery now supports column aliases in SELECT and WHERE clauses (GA opt-in), avoiding nested subqueries and simplifying migrations from Snowflake and Teradata. Cross-region table copying is also now generally available to streamline regional data replication and disaster recovery. Review the BigQuery release notes.
Conversational analytics in BigQuery expands to Canada and adds CSV/PDF export
Conversational analytics in BigQuery is now available in Montréal (northamerica-northeast1) and Toronto (northamerica-northeast2) to support Canadian data residency requirements. You can also now export agent responses — including text, tables, charts, and citations — directly to CSV or PDF (GA). Learn more about conversational analytics in BigQuery.
AlloyDB Omni 18.4.0 is now GA with Advanced Query Insights and external KMS support
AlloyDB Omni 18.4.0 brings PostgreSQL 18.4 to self-hosted and multi-cloud environments, adding the google_insights_v2 extension for granular query execution and wait-event diagnostics. It also adds Thales CipherTrust Manager support for Transparent Data Encryption (TDE) alongside HashiCorp Vault. Explore the AlloyDB Omni release notes.
Cloud SQL for PostgreSQL adds managed connection pooling and Private Service Connect defaults
Cloud SQL for PostgreSQL now supports enabling built-in managed connection pooling on existing Private Service Connect (PSC) instances to scale high-concurrency workloads. PSC is also now the default private IP option for new Cloud SQL instances in new projects to simplify cross-project networking. Review Cloud SQL updates.
Customer momentum: Unico prevents over $5B in fraud on Google's Agentic Data Cloud
Digital identity platform Unico migrated from Databricks Spark/Scala clusters to BigQuery and Spanner at the core of its data architecture. Unifying real-time transaction streams with petabyte-scale analytics enabled low-latency identity verification that has prevented over $5 billion in fraud. Read the Unico case study.
Developer guide: Getting started with Looker Data Apps
Following the debut of Looker Data Apps in preview, a new walkthrough on the Google Developer Community shares practical patterns for building prompt-driven applications inside Looker. Read the guide on the Google Developer Community.

October 12 - October 16

Looker Data Apps are now in Preview to build and host interactive, agent-generated applications natively inside Looker
Generated using natural language prompts, the Antigravity CLI, or Claude Code, these interactive web applications, natively hosted inside Looker, transform business reporting into operational systems of action. Users can test scenarios, adjust parameters, and trigger workflows in downstream platforms while inheriting LookML semantic modeling, row-level security, and access controls with no runtime LLM token costs. Read the announcement blog.
Build end-to-end data science pipelines with the Data Agent Kit
Following the general availability of the Data Agent Kit, a new end-to-end walkthrough demonstrates how data teams can build, test, and schedule machine learning pipelines directly from their preferred code editors. Explore the walkthrough and codelab.
Manage dashboard agents as code to bring CI/CD and version control to LookML dashboards
Data teams can now declare agent parameters, configurations, and prompt instructions alongside dashboard files in code. This integration enables standard developer workflows for conversational BI, including Git version control, peer review, branching, and automated CI/CD validation before agent modifications reach production end users. Read the technical guide on the Google Developer Community.
Simplify AI search with native AlloyDB hybrid search and Reciprocal Rank Fusion
AlloyDB for PostgreSQL simplifies hybrid search architectures by combining vector similarity search and keyword BM25 retrieval within a unified database engine. Using Reciprocal Rank Fusion (RRF), developers can merge lexical scoring and semantic embeddings into a single ranked result without writing a complex SQL query. This unified approach removes the operational overhead of running and synchronizing external search engines alongside operational databases for retrieval-augmented generation (RAG) and search applications. Explore hybrid search in AlloyDB.

October 5 - October 9

Data Agent Kit is now generally available to bring Google Data Cloud to any coding agent
Data Agent Kit provides a free collection of Model Context Protocol (MCP) tools and Google-authored agent skills that connect more than 15 Google Data Cloud services directly to coding agents across VS Code, Antigravity, Cursor, Claude Code, and Codex. This release expands support for developers and data practitioners to inspect schemas, author queries, and build end-to-end data pipelines in natural language directly from their IDE or CLI. Read the announcement blog.
Spanner Omni is now generally available for deploy-anywhere, multi-model workloads
Spanner Omni brings Spanner's distributed SQL, graph, vector, and key-value capabilities to any environment, including on-premises data centers, other public clouds via Kubernetes or VMs, and local developer laptops. Organizations can build and run multi-model applications with consistent APIs and query engines across hybrid and multi-cloud footprints while seamlessly scaling to managed Spanner on Google Cloud. Read the launch blog and explore how Spanner also removed cumulative mutation limits for DML transactions.
AlloyDB delivers PostgreSQL for agents and native BM25 hybrid search
We announced PostgreSQL for agents in AlloyDB, introducing an agentic database architecture engineered for real-time data access at agent scale with workload isolation. In addition, native BM25 ranking is now in Preview for AlloyDB and Cloud SQL, allowing developers to combine keyword search and vector embeddings directly in PostgreSQL without maintaining an external search engine. For hybrid deployments, the AlloyDB Omni RPM Orchestrator is now generally available, and you can dive deeper in our new Build AI apps and agents with AlloyDB eBook.
Lakehouse runtime catalog adds regional endpoints across 22 regions and GA support for flexible column names
Lakehouse runtime catalog regional endpoints are now live across 22 Google Cloud regions across the Americas, Europe, Asia-Pacific, and the Middle East, supporting both the Apache Iceberg REST catalog endpoint and the Apache Hive catalog endpoint to help customers meet strict data residency and sovereignty requirements. In addition, flexible column names for Apache Iceberg tables are now generally available by default across Iceberg external tables and managed tables, enabling seamless querying of columns containing spaces, hyphens, and Unicode characters. We also announced the Preview of cross-cloud caching and connections for the borderless Lakehouse.
Unlock predictive and agent-ready insights with BigQuery augmented analytics TVFs, TabFM, and identity columns
BigQuery introduced augmented analytics table-valued functions (TVFs): six built-in functions that automate metric diagnostics, trend analysis, and pattern discovery directly where your data lives and integrate as skills for AI agents. We also introduced TabFM in BigQuery to bring tabular foundation model capabilities to predictive analytics, alongside BigQuery identity columns to automatically generate sequential surrogate keys and simplify data warehouse pipelines.
Dataflow adds Pause/Resume and NVIDIA RTX PRO 6000 Blackwell GPU support for large-scale AI workloads
New Dataflow capabilities make it easier to run high-throughput streaming and batch AI pipelines at scale, including Pause/Resume job controls and support for NVIDIA RTX PRO 6000 Blackwell GPUs. And for teams running Apache Spark workloads, learn how to mitigate compute stockouts and maximize availability using flexible VMs.
Unlock up to 3x higher QPS and microsecond latency with Memorystore for Valkey 9.1
Memorystore for Valkey 9.1 is now available, delivering up to 3x higher queries per second (QPS) and microsecond-level caching latencies to accelerate high-throughput applications and real-time AI agent state management. Read the blog.
Customer momentum across Google Data Cloud — Yahoo, PayPal, Surescripts, Airwallex, and Lucius AI
Organizations across industries are scaling their data and AI foundations on Google Data Cloud. Yahoo uses flexible VMs in Managed Service for Apache Spark to reduce provisioning failures by 85%, while PayPal migrated mission-critical analytics workloads to Managed Service for Apache Spark to scale and reduce operational overhead. On the database front, Surescripts migrated 30.5 billion annual healthcare transactions to AlloyDB with 99.998% uptime, Airwallex manages more than 5,000 databases with 99.99% availability on Cloud SQL with a team of four DBAs, and Lucius AI runs a 210,000-tender global platform on AlloyDB and MCP with 47x faster ScaNN vector search.

September 7 - September 10

Pub/Sub SMTs can now AI Inference your Gemini Enterprise Agent Platform models!
Pub/Sub AI Inference SMTs allow you to apply inference on an incoming stream of events using models hosted in Gemini Enterprise Agent Platform. The model’s prediction is appended to your event, making it available for downstream processing in your data warehouse (like BigQuery) or operational database (like BigTable). This feature, now generally available, can dramatically simplify or enhance anomaly detection systems you are operating.
PostgreSQL Source Connector is now generally available in Managed Service for Apache Kafka!
Managed Service for Apache Kafka’s PostgreSQL connector allows customers to capture changes from their PostgreSQL database and ingest them into their Kafka infrastructure with low latency. This source connector is compatible with Cloud SQL for Postgres, AlloyDB, and self-managed PostgreSQL databases. Try this along with our entire portfolio of managed connectors, including MirrorMaker 2.0, BigQuery, Cloud Storage, and Pub/Sub! E-mail kafka-hotline@google.com if you have questions or feedback!

Pause-on-failure for Dataflow batch jobs is GA
Dataflow pause-on-failure enables you to preserve the state of a batch Dataflow job before it fails. By pausing your Dataflow job, you can address issues that are external to the pipeline and resume processing without losing completed work. This helps you better manage resource costs and improve job reliability when you face temporary outages or capacity constraints.

The insertAll API is now the BigQuery Storage Write API (REST)
The legacy insertAll streaming API is now rebranded as the BigQuery Storage Write API (REST). By dropping the "legacy" label, developers can confidently build long-term HTTP-based streaming workflows. This stateless JSON-over-HTTPS endpoint offers a lightweight alternative to heavy gRPC libraries—ideal for serverless web apps, IoT telemetry, and AI logging. The transition is seamless for existing users, requiring zero code changes and offering 100% backward compatibility. However, the Storage Write API (gRPC) version remains the recommended standard for high-throughput, continuous pipelines.

August 31 - September 4

Stateful processing is available in BigQuery continuous queries in Preview
Stateful operations significantly expand what’s possible with BigQuery continuous queries. This feature allows users to leverage functions like JOINs, aggregations, and windowing functions directly in their streaming queries. Now you can calculate metrics over time (for example, a 30-minute average) to power your downstream applications and AI agents with much richer, real-time signals.

Try out our feature here and share your feedback with bq-continuous-queries-feedback@google.com!
Synthetic data generator tool is available for Managed Service for Kafka
You’ve launched your first Kafka cluster. Now what? The next thing to do is to produce some data to the cluster, but that involves modifying a client application somewhere or spinning up a virtual machine. The synthetic data generator tool, now generally available, can start sending mock data to your cluster in 3 clicks, and will get data streaming into your cluster in less than two minutes. The perfect utility for those moments you just want to test your cluster and new features. Try our quickstart today!

Dataflow pipeline updates are faster & more flexible
Dataflow pipeline updates can now stop-and-replace pipelines, a major addition to the existing in-place-update feature. The new parallel pipeline option accelerates the migration between the old & new pipeline, resulting in reduced disruption to your business. You can also set a timeout on drains that prevents runaway costs for your pipeliness in the event of stuck processing. This feature is generally available. Try it here!

July 6 - July 10

New Lakehouse managed tables now in preview
Lakehouse tables for Apache Iceberg are now in preview and available in the console. By using Google-managed Apache Iceberg tables in Lakehouse, you can eliminate the overhead of maintaining duplicate data pipelines and complex synchronization logic between BigQuery and open-source engines. This unified table format delivers native, multi-engine read and write interoperability, allowing you to run concurrent DML/DDL operations across diverse analytics tools on a single, shared storage layer. Built-in automated table management handles painful background optimization tasks like compaction and partition tuning, freeing up your team to focus on building rather than managing storage maintenance.

June 1 - June 5

Beyond the Query: Powering AI Agents with Bigtable, Firestore & Memorystore
Discover the latest advancements in Google Cloud's NoSQL Database portfolio, including Bigtable, Firestore, and Memorystore. This series is designed for a broad audience: whether you are exploring these databases for the first time or are an existing user looking to leverage the new capabilities announced at Next '26.

Register here to secure your spot!

Cloud Engineer's AI Toolkit Workshops: Solve data-driven challenges with BigQuery, AlloyDB, Gemini and more. Hosted by Google Cloud Labs, this highly technical event is built specifically for Platform Engineers, SREs, and cloud infrastructure teams ready to bridge the gap between AI prototypes and production-grade deployments. Look out for more locations coming soon

Toronto - June 25 (Data Cloud) | RSVP Here
Chicago - June 30 (Data Cloud) | RSVP Here
Start a 10-day Bigtable free trial with a 1 node SSD cluster and up to 500GB of storage capacity. With no credit card required to start, you can easily ingest workloads and manage workloads that require low-latency, high-throughput, and predictable access. Plus, new Google Cloud customers get $300 in free credits on signup.

May 11 - May 15

Managed Service for Apache Airflow has launched a wave of new features, including the general availability of Airflow 3.1, AI-powered agentic troubleshooting, a new managed Airflow MCP Server for custom agent integration, and declarative YAML-based orchestration pipelines—discover all the details in the full blog post.

April 20 - April 24

Google-built ODBC Driver for BigQuery is now available in Preview
We are excited to announce the launch of the new, Google-built ODBC driver for BigQuery. This new open-source driver provides a direct, high-performance connection for applications to BigQuery and is developed entirely in-house by Google. Download a new driver and connect your application to BigQuery.

April 13 - April 17

We announced we are reintroducing Data Studio to play a significant role in the AI era, expanding from data visualizations and reports to host BigQuery conversational agents and data apps built in Colab notebooks.
We announced BigQuery Graph is now available in preview, offering an easy-to-use, highly scalable graph analytics solution, empowering data professionals to model, analyze and visualize massive-scale relationships in an entirely new way.

April 6 - April 10

We introduced Conversational Analytics for Looker Embedded environments, enabling users to add natural language experiences to their own custom data-driven applications, powered by Gemini.
We expanded Looker’s capabilities for faster ad-hoc analysis, with the introduction of self-service Explores, enabling you to bring your own data to Looker’s semantic layer and gain instant access to insights in a governed data environment.

March 23 - March 27

We showed you how you can scale your reads with Cloud SQL autoscaling read pools. This feature allows you to provision multiple read replicas that are accessible via a single read endpoint and to dynamically adjust your read capability based on real-time application needs.
Our customers are leveraging the full power of Conversational Analytics and Looker to drive major business and technical breakthroughs in the AI era. Companies like Telenor, Pet Circle, Fluent Commerce, Lighthouse Intelligence, Wego, and ROLLER are turning data into insights and actions, grounded by Looker’s semantic layer.

March 16 - March 20

We introduced an enhanced Gemini assistant in BigQuery Studio, transforming the agent from a code assistant into a fully context-aware analytics partner.

February 23 - February 27

We introduced managed and remote MCP support for Google Cloud databases, including AlloyDB, Spanner, Cloud SQL, Bigtable and Firestore, to power the next generation of agents. This announcement extends the ability for AI models to plan, build, and solve complex problems, connecting to the database tools our customers leverage daily as the backbone of their work environment.
We outlined how you can build a conversational agent in BigQuery using the Conversational Analytics API to help you build context-aware agents that can understand natural language, query your BigQuery data, and deliver answers in text, tables, and visual charts.

February 16 - February 20

Our customers are leveraging the full power of Looker to drive major business and technical breakthroughs. Companies like Arrive, Audika, Carousell, Framebridge, GumGum, Intel, Overdose Digital, Ocean Network Express, Subskribe and Promevo are leveraging Looker’s newest AI-driven capabilities, including Conversational Analytics, to transform data to insights and actions, and empower their entire organization with a single source of truth, powered by Looker’s semantic layer.

February 2 - February 6

Join us on March 4 for our webinar, Win Your AI Strategy with Cloud SQL Enterprise Plus, to learn how to power your generative AI workloads with 3x higher performance and 99.99% availability. Register today to discover how to build a scalable, enterprise-grade foundation for your most demanding AI applications.

January 26 - January 30

We introduced Conversational Analytics in BigQuery, which allows users to analyze data using natural language. Conversational Analytics in BigQuery is an intelligent agent that generates, executes and visualizes answers grounded in your business context directly in BigQuery Studio, making data insights for data professionals more conversational.
We outlined how data products have become the foundation for AI agents, providing the context needed to make autonomous agents reliable and trusted for real business use, backed by organized business logic and semantic understanding.
We highlighted how you can supercharge data analytics workflows, and outlined Google Cloud’s AI agent offerings for data engineering, data science, and development tools, so you can integrate agentic workflows in your applications, empower your teams and speed discovery.

January 19 - January 23

We have fundamentally reimagined Firestore with pipeline operations for Enterprise edition. Experience a powerful new engine featuring over a hundred new query features, index-less queries, new index types, and observability tooling to improve query performance. Seamlessly migrate using built-in tools and leverage Firestore’s existing differentiated serverless foundation, virtually unlimited scale, and industry-leading SLA. Join a community of 600K developers to craft expressive applications that maximize the benefits of rich queryability, real-time listen queries, robust offline caching, and cutting-edge AI-assistive coding integrations.
Introducing Google Cloud SQL on MSSQLTips: We are highlighting a new technical guide published on MSSQLTips titled "Introducing Google Cloud SQL." This article serves as an essential resource for SQL Server administrators and developers exploring Google Cloud's fully managed database service. It provides a detailed overview of Cloud SQL capabilities, including high availability, security integration, and the seamless transition of on-premises SQL Server workloads to the cloud, making it an ideal resource for those planning their migration strategy.
We are excited to announce the Public Preview of Microsoft Entra ID (formerly Azure Active Directory) integration with Cloud SQL for SQL Server. Designed to tackle the challenge of identity sprawl in multi-cloud environments, this integration allows organizations to govern database access using their existing Microsoft identity infrastructure. Key benefits include centralized identity management, enhanced security features like Multi-Factor Authentication (MFA), and simplified user administration through direct group mapping. This feature is available for SQL Server 2022 and supports both public and private IP configurations.

January 12 - January 16

Google-built JDBC Driver for BigQuery is now available in Preview
We are excited to announce the launch of the new, Google-built JDBC driver for BigQuery. This new open-source driver provides a direct, high-performance connection for Java applications to BigQuery and is developed entirely in-house by Google. Download a new driver and connect your Java application to BigQuery.
Troubleshoot Airflow tasks instantly with Gemini Cloud Assist investigations: Cloud Composer just got smarter. We are excited to announce that Gemini Cloud Assist investigations are now available directly within Cloud Composer 3. Instead of manually sifting through raw logs, you can now simply click "Investigate" on a failed Airflow task. Gemini analyzes logs and task metadata to identify failure patterns—such as resource exhaustion or timeouts—and provides actionable recommendations driven by Gemini Cloud Assist to resolve the issue. This integration shifts the debugging experience from manual toil to automated root cause analysis, significantly reducing the time required to restore your pipelines. Learn more about AI-assisted troubleshooting.

Unifying enterprise context and semantics: LookML in Knowledge Catalog is now GA

Mon, 26 Oct 2026 16:00:00 +0000

As enterprises go from experimental AI agent prototypes to using them in production, data teams are realizing that access to data is not the same as understanding that data. To help bridge that gap, today, we are announcing the general availability (GA) of LookML semantic language indexing in Knowledge Catalog for Looker Core on Google Cloud.

To answer a complex business question accurately, an AI agent needs context — table schemas, data lineage, governance policies, business definitions, etc.. However, context is not a monolith; knowing where data lives is very different from knowing how to calculate metrics with it. In the enterprise, teams often conflate broad organizational context (usually in the form of metadata from structured and unstructured documents) with deterministic business semantics (the exact SQL formulas, join paths, and filtering rules that define metrics like net revenue or churn), assuming that a single data catalog or a business intelligence (BI) tool handling both enterprise-wide asset discovery and precise metric calculation.

In reality, no one tool can carry the entire load for agentic AI, as enterprise agents require both organizational context and deterministic business semantics. In Google Cloud environments, those are provided by Knowledge Catalog and LookML:

Broad enterprise context with Knowledge Catalog: Agents need a unified context graph that aggregates metadata across structured tables and unstructured documents. Through Knowledge Catalog, agents discover assets, inspect data quality, trace end-to-end lineage, respect column-level privacy tags, and surface corporate policy documents. The context graph tells an agent what data exists, how it is governed, and how it is connected.
Deterministic mathematical semantics with LookML: What a catalog does not author on its own is how the numbers are computed. Describing a table is not the same as defining how tables join or how metrics are calculated. Looker's modeling language, LookML, encodes production-tested semantic logic — multi-grain join protections, conditional filters, and exact KPI formulas — so agents don't guess SQL math at runtime.

With this native integration, the LookML business logic that powers your trusted BI reporting is automatically indexed into Knowledge Catalog — giving Gemini Enterprise and custom AI agents both enterprise-wide discovery and deterministic BI semantics.

Why a context graph needs deterministic semantics

When an LLM agent connects directly to raw database tables — even when armed with basic table descriptions or a business glossary — it still has to infer mathematical relationships on the fly. Without explicit semantic rules, agents routinely fall into two traps:

Metric definition drift: Standard text-to-SQL agents can invent conflicting formulas for foundational KPIs like net revenue or customer acquisition cost, failing to apply domain-specific accounting rules (such as recognizing revenue only when order_status = 'Complete' or prorating annual contracts across active service days).
Naive join fan-outs: Complex schemas often contain one-to-many (1:N) and zero-to-many (0:N) relationships across invoices, line items, and refunds. Without pre-modeled join relationships and symmetric aggregation rules, agent-generated SQL can multiply rows and inflate financial totals.

Knowledge Catalog and Looker solve this together, enabling you to model once in LookML and access those semantics everywhere across Google Cloud's AI ecosystem. Knowledge Catalog enforces semantic math when that logic is defined in a semantic modeling tool; LookML provides a mature, production-proven language for defining those exact mathematical relationships.

What's new: LookML in Knowledge Catalog

Previously, catalog integrations only captured shallow, file-level pointers and basic dashboard inventories. With this release, LookML's deterministic semantics become a first-class layer in the Knowledge Catalog context graph. An agent that finds a metric in the graph now also finds its exact definition, its join path, and its lineage back to source tables — in the same place, under the same governance. Synchronization is always on by default with no action required from end users, and with live sync, any update pushed to production LookML is reflected in Knowledge Catalog in near-real-time so agents operate on the freshest business logic.

Because this metadata is indexed directly into Knowledge Catalog by default, it is immediately available to Gemini Enterprise, without custom agent code or separate catalog data stores. When connected to Knowledge Catalog, Gemini Enterprise uses built-in read-only skills and hybrid search to look up technical schemas, business glossaries, and indexed LookML logic in a single step, passing that rich context directly to your federated BigQuery connector to generate accurate SQL. (For developers building custom agents, the same context bundles are also accessible via the LookupContext API.)

Knowledge Catalog now captures and links the following entity levels across Looker Core on Google Cloud:

LookML models (looker-model): Captures descriptive metadata, database connection routing, include hierarchies, data validation tests (assert), and parent-child EntryLinks mapping models to their associated Explores, Views, and LookML dashboards.
LookML Explores (looker-explore): Indexes full relational join logic (type, sql_on, relationship cardinality such as many_to_one fan-out protection, foreign_key, required_joins, and sql_where) as Schema-Join relationships. It also captures mandatory filter guardrails (always_filter, access_filter), inherited dimensions and measures, and predefined starter queries that help agents and analysts overcome the cold-start problem.
LookML Views and fields (looker-view): Extracts column-level compiled LookML SQL expressions (for example, ${total_revenue} / NULLIF(${count}, 0)), primary keys, dimension group timeframes, field synonyms, and derived table transformation logic (including SQL and native derived tables, partition keys, and cluster keys).
Dashboards, Looks, and tiles (looker-dashboard, looker-dashboard-element, looker-look): Catalogs both UI-created and LookML-defined dashboards and visualization tiles, complete with dashboard filters, chart types, ownership, and usage statistics. Users in Knowledge Catalog are able to see table- and column-level lineage from underlying BigQuery tables all the way to individual dashboard tiles, allowing data engineers to evaluate schema changes proactively, before disruptions occur.

Looker Core on Google Cloud automatically indexes resolved LookML models, Explores, Views, lineage, and dashboards directly into Knowledge Catalog.

Three patterns for data agents

Bringing LookML into Knowledge Catalog gives Gemini Enterprise (and your broader agent ecosystem) a single place to retrieve both broad organizational context and governance from Knowledge Catalog and explicit mathematical relationships and SQL definitions authored in LookML.

To see the impact of combining these two layers in Gemini Enterprise, compare how three architectures handle a complex cross-domain prompt: "What was our total revenue last fiscal quarter by product category, and which regional promotional policy limits apply?"

Agent architecture

System mechanics

Data logic and governance resolution

User experience and token efficiency

Pattern 1: Baseline database agent

Data warehouse only

Direct LLM writing raw SQL against warehouse tables with no semantic or catalog context.

Ungoverned and non-deterministic

Has no visibility into data quality, PII tags, or corporate documents, and guesses SQL metric logic blindly — assuming a standard January fiscal calendar, summing uncompleted orders, and exposing restricted customer PII.

Fragile and high-risk

Prone to schema hallucinations, fails on questions requiring unstructured policy context, and burns tokens in multi-turn SQL debugging loops.

Pattern 2: Context-grounded agent

Knowledge Catalog without indexed semantics

Gemini Enterprise or another agent platform uses Knowledge Catalog to look up table descriptions, glossary terms, PII governance aspects, and unstructured policy PDFs, then writes SQL via the warehouse connector.

Context-grounded, but lacks semantic SQL logic

Enforces Knowledge Catalog governance and retrieves unstructured promotional policy rules, but lacks semantic definitions for SQL metrics and table relationships. This approach can miss conditional accounting rules (like order_status = 'Complete') or cause join fan-outs across multi-grain tables.

Governed discovery, inconsistent math

Solves asset discovery, compliance, and unstructured context, but still suffers from text-to-SQL calculation drift and multi-turn SQL retries.

Pattern 3: Context + semantic-grounded agent

Knowledge Catalog + Looker Core on Google Cloud with indexed LookML

Gemini Enterprise or another agent platform connects to Knowledge Catalog enriched with indexed LookML metadata, automatically retrieving both broad catalog context and LookML join paths and field formulas to ground its SQL generation.

Governed context plus semantic SQL definitions

Enforces Knowledge Catalog PII aspects, retrieves unstructured policy PDFs, and grounds its SQL in LookML's exact mathematical definitions indexed in Knowledge Catalog, applying status = 'Complete', the May–July fiscal calendar, and predefined join conditions across warehouse tables.

Accurate and token-efficient:

Pre-mapped LookML join blueprints and compiled SQL expressions replace guessing, prevent join fan-outs, reduce SQL retry loops, and let the agent point users to relevant Looker dashboards and Explores.

When you follow pattern 3 and pair Knowledge Catalog with Looker's semantic layer in an enterprise agent workflow, the agent moves from guessing at raw SQL to a governed, two-stage execution flow:

Context and governance discovery in Knowledge Catalog: Before touching any data, the agent searches Knowledge Catalog, verifies that the thelook_ecommerce LookML model is gold-certified, checks column-level governance tags, and maps the prompt to exact LookML dimensions and measures, converting natural language into structured business intent.
Deterministic semantic execution: Rather than having the LLM invent SQL on the fly, the agent grounds its SQL in LookML's exact mathematical definitions indexed in Knowledge Catalog and returns trusted, repeatable figures.

Alongside the answer, the agent returns an interactive Open in Looker Explore link with the exact measures and filters pre-populated, letting users slice, dice, and inspect row-level line items in a single click.

Side-by-side comparison of a baseline agent querying BigQuery directly (left), which guesses at raw SQL and fiscal dates, versus an agent governed by Knowledge Catalog and Looker (right), which applies indexed LookML definitions and returns an interactive "Open in Looker Explore" link.

Scaling agents for BI and the enterprise

A common question from data leaders is: "If we use Looker Conversational Analytics, when do we use an enterprise agent grounded in Knowledge Catalog?"

The two experiences are designed for complementary jobs across your organization:

Looker Conversational Analytics is your dedicated BI assistant: When business users are working inside BI workflows and asking questions scoped to a focused set of curated Looker Explores, Conversational Analytics delivers immediate, self-service data exploration and visualization directly within Looker.
Knowledge Catalog + Looker powers your enterprise-wide agents: When users ask questions in Gemini Enterprise across federated Data Cloud sources like BigQuery, they often span structured tables, BI metrics, and unstructured documents. Enabling Knowledge Catalog in Gemini Enterprise gives the assistant built-in skills to dynamically discover relevant semantics across hundreds of LookML models, pass indexed LookML join definitions, metric SQL formulas, and starter queries to the BigQuery connector to prevent join hallucinations, and enforce centralized governance aspects and IAM.

Getting started

Because Looker Core on Google Cloud, Knowledge Catalog, and Gemini Enterprise are natively integrated, you can connect your existing LookML business logic to your enterprise AI assistant in just a few clicks, with no custom agent code required.

To start discovering Looker semantics and grounding your agents:

Confirm first-party metadata indexing is enabled for Looker Core on Google Cloud. This is enabled by default for new instances, with no additional charge for storing Looker technical metadata. Once enabled, metadata synchronization into Knowledge Catalog is always on by default (across both initial indexing and ongoing live updates). Enable the Cloud Dataplex API and Data Lineage API in your project and grant users the required view roles (roles/dataplex.viewer, roles/looker.schemaViewer, and roles/datalineage.viewer) to have your resolved LookML models, Explores, Views, join paths, and dashboards automatically sync into Knowledge Catalog.
Enable Knowledge Catalog in Gemini Enterprise. When you attach a BigQuery (or Spanner, Cloud SQL, or AlloyDB) data store in federated query mode to your Gemini Enterprise app, the Knowledge Catalog integration is enabled automatically. You can also turn it on or off at any time in the Google Cloud console by opening your Gemini Enterprise app, navigating to Configurations > Assistant, toggling on Enable Knowledge Catalog, and clicking Save and publish.
Ask questions. Gemini Enterprise connects to Knowledge Catalog using end-user credentials, meaning users only discover and query data assets they have IAM permission to view (such as roles/bigquery.dataViewer). Behind the scenes, the assistant automatically looks up your indexed LookML definitions, join logic, and starter queries in Knowledge Catalog and passes them to your federated connector to deliver accurate, governed answers.

To dive deeper, read the Manage Looker Core on Google Cloud with Knowledge Catalog documentation and learn how to Connect to Knowledge Catalog in Gemini Enterprise. To build a more customized experience, explore our hands-on ADK agent codelab on GitHub.

Trusted Data Apps for the Agentic Data Cloud, powered by Looker

Mon, 12 Oct 2026 16:00:00 +0000

As a business intelligence user, this probably sounds familiar: Most dashboards only provide you with a standardized canvas and a fixed library of charts, and the “analysis” consists of a CSV export that takes you to another system. Or sometimes it's designing the dashboard that’s the challenge: You can picture the executive briefing, the customer-facing portal, or the interactive explainer that you want, but can’t assemble it out of the available grid tiles.

Either way, the solution has always been to build a custom application. The advent of AI has sped up the process — a working prototype now takes an afternoon — but the excitement of that first demo fades fast. Without access to governed data, row-level security, an infrastructure owner, or a clear path through security review, a tool written by an LLM is simply a prototype that can never go to production.

Today, to bridge this gap, we are announcing Looker Data Apps (in preview) — pairing the speed of agent-generated applications with the governance, security, and trust of your existing Looker governance framework. Describe what you need in plain language, and the agent builds it as a real, production-grade application hosted natively inside Looker!

What is a data app?

A data app is a bespoke, interactive web application generated by an AI agent and hosted natively within Looker. While dashboards provide top-down visibility, data apps serve as an active system of action designed around a specific operational workflow. A data app:

Goes beyond standard grid tiles with pixel-perfect, bespoke layouts configured for a specific operational role or job-to-be-done.
Enables business users to input parameters, test scenarios, run live simulations, and update underlying records.
Connects end-to-end business processes by connecting data analysis to business execution tasks like updating source records or dispatching events to tools like Salesforce and Jira.

Build pixel-perfect interfaces using AI developer tools like Claude Code or Antigravity CLI, grounded directly by Looker’s semantic layer.

Expanding your semantic layer into an application platform

Data apps do not replace dashboards; they expand the boundaries of what you can build on top of your existing dashboards and semantic layer.

Dashboards excel at aggregate visibility and passive reporting. Data apps handle active execution, letting users modify parameters and trigger downstream business tasks.
Dashboards provide consistency using standardized chart libraries. Data apps deliver broad flexibility, unlocking custom web components and specialized interactions.
Data apps are grounded in governed data to ensure accuracy. Whether users are reviewing a KPI dashboard or adjusting budgets in a data app, all logic is governed centrally with the LookML semantic layer to eliminate metric discrepancies and hallucinations.

Transform analysis into action with customizable, multi-page web app experiences and embedded operational workflows.

Built for enterprise scale

We designed Looker Data Apps for demanding, governed enterprises.

Every measure is grounded in the semantic layer. Every app you build directly inherits your existing semantic models, row-level security, and access controls. Calculations stay consistent across the enterprise, and built-in query inspection lets teams audit every line of generated SQL to eliminate metric hallucinations.
Zero-code by default. Deploy agent-generated React applications directly inside Looker with no code. Or users can use LLMs like Claude Code and Antigravity CLI to build interactive apps and deploy them as data apps in minutes.
No runtime token costs. LLM tokens are only consumed when you’re designing or generating the app. After that, every end-user interaction runs on native Looker APIs and models. Put a data app in front of 50 people or 5,000 and the marginal cost doesn't change.
As easy as embedding a dashboard. You can deploy your data apps into external portals using the same workflows you use to embed a dashboard. No new pipelines, no custom application code, no second permission models.

From prototype to production in minutes

The days of having to choose between basic dashboards and investing valuable engineering time to build bespoke systems are coming to an end. Instead of letting prototypes stall in sandboxes due to compliance and security barriers, Looker Data Apps allows teams to deploy governed, production-grade tools in minutes.

Looker Data Apps are now in preview. Get started today.

Looker’s semantic layer governs Gemini Enterprise data for user trust

Tue, 11 Aug 2026 16:00:00 +0000

For organizations deploying AI agents at scale, there’s often a critical divide between structured and unstructured data. While large language models (LLMs) excel at parsing text documents, emails, and PDFs, they can struggle when presented with raw enterprise databases. Meanwhile, standard natural-language-to-SQL (NL2SQL) models often guess how database schemas fit together, which can lead to unpredictable queries, inconsistent metrics, and AI hallucinations that erode user trust.

Gemini Enterprise brings the best of Google AI to every employee through an intuitive chat interface that acts as a single front door for AI in the workplace. And now, Looker’s governed semantic layer serves as the trusted foundation for structured data within Gemini Enterprise, enabling trusted self-service business intelligence for all Gemini Enterprise users. With this integration, Looker analysts and admins can publish conversational agents natively into their Gemini Enterprise environments via the Agent-to-Agent (A2A) protocol. Now, organizations can provide their AI-accelerated taskforce with robust and trusted tools, powered by real-time analytics, that they can explore in natural language in addition to their daily workspace workflows. Making it easy to offer conversational agents in Gemini Enterprise expands discoverability and promotes a data-driven culture, while reducing friction to adoption.

Bringing a semantic foundation to structured and unstructured data

By combining Looker’s semantic layer with Gemini Enterprise, you can query both structured databases and unstructured documents in plain English, all in one place. Instead of jumping between dashboards and other tools to understand your numbers, teams can instantly connect hard metrics with real-world context to solve problems and make decisions faster.

Publishing Looker agents for consumption in Gemini Enterprise

Minimize AI hallucinations

If you ask the typical AI chatbot to calculate "revenue" or "churn rate" against an unstructured cloud database, it has to guess which tables to join, which filters to apply, and which timestamps to trust. This can result in different people asking the same question, only to get completely different answers.

Looker’s semantic layer eliminates this guesswork, serving critical context to Gemini Enterprise in the form of codified data, allowing the agent to give deterministic, predictable responses.

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When a Gemini Enterprise user requests a business KPI in Gemini Enterprise, the request is routed directly to a Looker agent. The semantic layer generates deterministic, precise SQL based on version-controlled business logic. This helps ensure when an executive asks for "Revenue," they get the exact, governed enterprise metric — not a guess.

Robust governance and secure access management

Data governance and security are critical when introducing AI to enterprise data warehouses. Organizations can’t risk corporate information being loosely ingested, indexed, or exposed outside of strict permissions.

Looker’s integration with Gemini Enterprise is built on a zero-risk pass-through architecture, processing the data, but not writing to persistent storage. Gemini Enterprise does not ingest, replicate, or store your underlying database records. Instead, the integration operates safely and securely over the A2A protocol, following these core tenets:

OAuth authorization: In order to interact with a Looker agent within Gemini Enterprise, end users provide a secure, one-time OAuth consent. This binds their Gemini session to their specific Looker credentials.
Strong governance enforcement: Because the architecture relies on live pass-through queries, Looker’s existing row-level and column-level access controls are maintained.
Strict security isolation: If a user does not have permission to view, say, sensitive regional payroll or financial rows within the Looker platform, the Looker agent actively restricts that data in the Gemini environment. Should an agent be published to the Agent Gallery to simplify discovery, it still does not bypass the security controls that you established.

Technical capabilities and enterprise readiness

Deploying Looker agents natively into Gemini Enterprise via the A2A protocol doesn't just make it smarter — it makes it more interactive and interoperable, without sacrificing security. Here are some of the features you’ll find in this release.

Rich visual interactivity: support for charts

They say a picture is worth a thousand words. When users interact with Looker agents inside Gemini Enterprise, the platform goes beyond textual explanations and provides native, interactive data charts. If a user asks for a visual trend—such as monthly sales performance or regional distribution—the Looker agent maps the database response with rich, presentation-ready visualizations directly inside the universal chat box.

Note: If you published Looker agents in Gemini Enterprise prior to Looker release 26.12, we recommend updating or refreshing them to take advantage of these enhanced visualization capabilities.

Interoperability with first- and third-party agents

Looker agents published to Gemini Enterprise can understand context across different agents and data sources. Leveraging standard communication frameworks, these agents can securely share structured, governed insights with other first-party Google Cloud agents like the Deep Research Agent or external third-party agents to create structured workflows. This enables complex multi-agent orchestration, where an enterprise operational agent can pull data from a Looker agent to feed into a separate productivity or supply-chain workflow.

Looker-based user authentication

To preserve enterprise governance, this integration implements a robust, identity-centric authentication model. Users are required to provide a one-time OAuth consent, binding their active Gemini Enterprise session securely to their underlying Looker credentials. This helps ensure that every conversational query hitting your databases is authenticated at the user level, enforcing pre-existing Looker permission structures, row-level data access filters, and column-level masking rules — no exceptions.

Trusted data in Gemini Enterprise

The future of work is agentic. Gemini Enterprise provides a single, secure architecture to deploy a global digital task force,empowering your business with the best of Google AI for developers, employees, and customers.

The integration of Looker with Gemini Enterprise not only brings trusted data analytics to business users but also adds rich interactivity, visual charts, and data storytelling directly into their everyday workspace. As business users embrace this agentic new way of working, they aren't just getting text answers; they are getting presentation-ready visualizations that bring operational metrics to life and deliver complex insights.

To get started, learn how to publish your data agents in Gemini Enterprise to make your agent’s predefined context and analytics available to your entire organization.

Automate data monitoring and root-cause analysis with Looker Agentic Workflows

Wed, 29 Jul 2026 16:00:00 +0000

Traditional business intelligence alerts can only tell you that a metric changed, leaving data analysts to manually hunt through dashboards to figure out why. Today, we are introducing Looker Agentic Workflows in preview, a new capability in Looker that automates both metric monitoring and root-cause analysis, using intelligent background agents.

With Conversational Analytics in Looker, teams can already query business data using natural language. Looker Agentic Workflows turns those ad-hoc questions into continuous, automated monitoring routines directly from the chat interface. You can set up an automation simply by prompting the agent, such as asking to "Monitor return rates weekly" or "Notify me if average order value exceeds $1,000." The agent interprets your intent, confirms specific threshold conditions, and generates a workflow configuration plan for you to review before launching the monitor.

Review the workflow plan generated inside the conversational analytics pane

Deliver root-cause analysis in Slack and email

When a metric crosses your defined threshold, the background agent does more than send a basic notification. It can automatically run a Key Driver Analysis (KDA) across the underlying data model to isolate specific factors driving the change, such as product categories or customer cohorts. The complete diagnostic summary is delivered directly into your team's workspace via Slack or email, eliminating the need for manual data hunting or analyst support tickets.

Automated root-cause analysis delivered with the metric change notification.

Investigate deeper with central oversight

Every notification includes a direct link back into Conversational Analytics in Looker. Clicking the link opens an interactive session pre-loaded with the agent's diagnostic findings, allowing you to ask follow-up questions and test hypotheses immediately. Balancing user flexibility with enterprise governance, Looker provides a centralized workflow management interface. Business users can view and edit their own active monitors, while Looker administrators retain full oversight to review, adjust, or disable workflows across the entire instance.

Central pane to review, edit, and manage workflows

Get started with Looker Agentic Workflows

Looker Agentic Workflows is available in preview for Looker version 26.08 and later. Administrators can activate the feature by opening the Gemini in Looker settings page and enabling the Agentic Workflows preview toggle. Once enabled, users with chat_with_agent and create_alerts permissions can build workflows immediately. Review the Looker documentation to configure your first workflow.

Bringing Conversational Analytics to your entire data ecosystem

Tue, 28 Jul 2026 17:30:00 +0000

Increasing the adoption of generative AI across the enterprise requires you to do more than deploy a generic chatbot with a custom wrapper. Interacting with business-critical databases demands absolute trust, strict governance, and deep grounding in enterprise semantics.

Over the last year, Conversational Analytics (CA) in Google Cloud has moved from isolated experiments to scaled, enterprise-wide deployments. BigQuery Conversational Analytics and the Conversational Analytics API are now generally available, adding to the general availability of Conversational Analytics in Looker last year. Building on this momentum, Conversational Analytics in Databases are also available in Preview. And so much more has happened — Google Cloud Conversational Analytics is available for more data, across more surfaces, with more enterprise controls, and greater capability than ever before.

Let’s take a deeper look at the state of Conversational Analytics in the Google Data Cloud — what you can do with it, the benefits that it brings, and how to get started with it today.

Query across multi-cloud and database workloads

Conversational Analytics is now generally available for BigQuery and Looker, and in preview for AlloyDB, Cloud SQL, and Spanner. You can also analyze data stored in Lakehouse Managed Service tables, Apache Iceberg REST catalogs, and federated AWS S3 Unity Catalogs. Whether your data resides exclusively in Google Cloud or across multiple cloud providers, your agents can query it natively.

For data practitioners, Conversational Analytics is integrated directly into BigQuery Studio, BigQuery Data Canvas, Database Studio and Data Agent Kit. For business teams, these conversational capabilities extend directly into Looker, Data Studio, and Gemini Enterprise. Data teams can publish Conversational Analytics agents created in BigQuery, Looker, AlloyDB, Spanner, and Cloud SQL directly into Gemini Enterprise, giving business leaders a centralized interface to query complex data safely.

Our APIs and MCP tools let you embed Conversational Analytics wherever your business users work, like custom applications and multi-agent systems, or as slack chatbot that can answer questions across data sources, as we showed at Google Cloud Next.

Enterprise security and governance controlsScaling generative AI to tens of thousands of users requires ironclad governance and transparent cost controls. Conversational Analytics includes Customer Managed Encryption Keys (CMEK), Private IP, and Virtual Private Cloud (VPC) controls.

We guarantee Data Residency (DRZ) at rest and machine learning processing inside multi-region endpoints within the European Union and the United States, along with HIPAA compliance. For data access, role-based controls, including parameterized secure views in AlloyDB for PostgreSQL, help ensure users chatting with an agent only see data they are authorized to view, enforced down to row- and column-level permissions.

Monitoring Conversational Analytics in BigQuery to track agent fleet health, active users, query volumes, and top knowledge sources.

As usage grows, administrators need tools to manage costs, observe system health, and improve accuracy. You can configure native cost controls to define limits on maximum query sizes in bytes, and track usage through BigQuery query labels and Looker system activity logs.

To maintain fleet visibility, agents can also export health, tool usage, latency, and token consumption metrics via OpenTelemetry (OTEL) standards. Integrated feedback loops allow administrators to review agent traces and user feedback, establishing a foundation for continuous evaluation and accuracy improvements over time.

Grounded context through agent and data co-designWrapping a generic LLM around an enterprise database can sometimes lead to hallucinated logic. To minimize this, we co-designed Conversational Analytics agents alongside the data platforms they query.

For instance, agents leverage Knowledge Catalog for data discovery, glossaries, and automated context enrichment like table joins and descriptions. BigQuery Graphs and Spanner Graphs allow agents to query structured and unstructured data across multi-hop relationships. Additionally, Looker’s semantic layer (LookML) grounds agent responses in centrally governed metric definitions, helping ensure answers remain deterministic rather than relying on guessed SQL joins.

Grounding Conversational Analytics across Knowledge Catalog, BigQuery Graph, and Looker’s semantic model helps ensure deterministic, enterprise-governed responses.

Conversational Analytics agents are also co-designed with the data they query. This means their tools are context-aware, to have the best understanding of the metadata. They also benefit from built-in capabilities like multimodal data querying using BigQuery object tables, operating over multimodal data with ai.search, ai.generate_embedding, ai.classify, ai.score and using ai.forecast and ai.detect_anomalies to use the TimesFM foundation for forecasting and anomaly detection.Additionally, ai.key_drivers performs automated contribution analysis to pinpoint exactly what is driving unexpected changes in your data. When integrated with Looker, these agents leverage the semantic layer to ground their responses in centrally governed, deterministic metrics. To avoid AI hallucinations, this API-first approach (using 'Golden Queries') ensures agents retrieve verified business logic rather than guessing at SQL joins. Looker additionally equips the agents to seamlessly navigate high-cardinality datasets with dynamic filtering, automatically enforce row-level security during the chat experience, and surface context-aware suggested questions.

Proactive insights with Agentic Workflows

Analytics is moving beyond reactive question-answering toward proactive intelligence. That is, instead of requiring users to ask the right question at the right time, Conversational Analytics agents can run multidimensional deep dives to analyze 10 to 20 contributing factors behind a change in a metric.

With Agentic Workflows, now in preview, you can schedule automated reporting routines delivered directly into your chat workflow. Agents continuously run anomaly detection across key metrics, sending daily or weekly summaries straight to your team. Streaming anomaly detection can also launch an agent automatically the moment a key metric deviates from baseline thresholds.

Running a multi-step deep dive in Conversational Analytics to automatically investigate complex data relationships across enterprise datasets.

Flexible integration with APIs, SDKs, and MCP

Conversational Analytics is available to developers and business users in their existing environments. The Conversational Analytics API includes native SDKs for Node.js, Java, Go, Python, PHP, Ruby, and .NET and keeps insights where the work happens. We are expanding how and where people use Conversational Analytics, starting with Looker Dashboards and Data Studio, as well as supporting publishing agents to Gemini Enterprise.

You can also add Conversational Analytics to other multi-agent systems. Using the Agent Development Kit (ADK) and Model Context Protocol (MCP), you can integrate Conversational Analytics into custom applications, Slack bots, or multi-agent orchestrators. For example, a supply chain orchestrator agent can query a financial data agent to calculate the margin impact of a shipping delay in real time.

Get started with Conversational Analytics

Google Cloud Conversational Analytics unifies your data estate, security control plane, and developer APIs to deliver proactive data insights wherever your team works. Explore our Conversational Analytics documentation, review our quickstart repositories, and sign up to try our new previews today.

Google named a Leader in 2026 Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms for third year in a row

Wed, 01 Jul 2026 16:00:00 +0000

For the third consecutive year, Google has been recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms. This recognition comes on the heels of Google Cloud Next 2026, where we feel we showcased a fundamental evolution in how enterprises interact with data: the shift from a reactive system of intelligence to a proactive system of action. As organizations rapidly evolve to incorporate autonomous AI into daily operations, Looker and Google are redefining the modern stack by bridging the gap between data insights and automated business workflows. By anchoring this agentic transition in a foundation of enterprise-grade trust, Looker’s Agentic solution continues to serve global organizations, from fast-growing startups to large enterprises, by transforming raw data into trusted, actionable business value.

Our momentum in this agentic era relies on two core pillars: a universal semantic layer that establishes a foundation of truth, and Gemini’s deep reasoning capabilities that turn that truth into autonomous business action.

Download the complimentary 2026 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms.

The universal semantic layer agents

At the core of the agentic transition is Looker’s semantic layer. In a world where hallucinated data and conflicting metrics can kill business, LookML is critical for customers such as YouTube, Telenor and Allo Fiber, deploying agents into production at scale and keeping them grounded in verified enterprise truth.

Key governance strengths include:

Unified analytics governance: Centralized, code-based semantic layer that guarantees metric consistency across the organization. Pairing this single source of truth with hierarchical permissions and a new certification framework controls content trust levels and enables proactive platform auditing.
Governed semantic layer for in-database analytic and graph modeling: Native integration with in-database analytic models, including BigQuery Graph and Snowflake semantic views, enables organizations to define, version-control, and manage complex relational and graph-based data relationships natively within LookML while maintaining a consistent semantic truth across external applications.
Enterprise lifecycle management: Native, git-based version control supports continuous integration testing and seamless multi-environment management before production deployment.
High concurrency architecture: Elastic resource allocation mitigates usage spikes, allowing teams to deliver scalable insights during heavy user demand.

Powered by Gemini’s reasoning capabilities

True agentic business intelligence requires deep cognitive reasoning. Looker’s purpose-built BI generative AI capabilities with Gemini 3 serve as Looker’s native AI fabric, unlocking two significant advantages across the enterprise:

For business users: It enables complex, multi-layered strategic analysis through advanced, natural-language abstract reasoning.
For developers: It embeds directly into the daily workflow to dramatically accelerate analytics engineering with reliable LookML auditing and automated code writing.

Leaping into the Agentic BI era with Looker and Gemini

Gemini is powering the reimagination of the Looker stack from agentic semantic modeling, data explorations, Dashboard Agents, to Conversational Analytics. At Google Cloud Next 2026, we showcased our latest Looker product innovations:

Looker everywhere: We are expanding Looker's footprint beyond traditional interfaces through headless BI architectures, highlighted by Looker’s Managed MCP offering. This capability brings our semantic intelligence directly to external platforms, as showcased by PayPal, which successfully scaled accurate conversational analytics to 3,000+ users via Claude Desktop and Looker MCP. Developers can also leverage the Conversational Analytics API to build, secure, and deploy custom, trusted data agents within any proprietary application or third-party agent platform.
Looker BI Agents: Our specialized conversational agents allow users to query complex data models across applications using plain natural language, while dashboard agents embed these interactive conversational experiences directly into existing dashboards. Fully integrated with Gemini Enterprise, these agents can be deployed straight into your corporate workspace workflows. Furthermore, across all environments, these agents enable users to orchestrate autonomous agentic workflows and monitor execution, helping ensure teams can interact with LookML-governed metrics and trigger automated actions right where they already cooperate.
AI-powered self service: We’ve completely reimagined Explore Mode, combining an intuitive drag-and-drop canvas with conversational analytics. Tools like the Visualization Assistant use natural language to design beautiful charts on the fly, while the Insight Assistant auto-generates narratives to surface key trends in seconds. This sits alongside now generally available (GA) paginated reporting and tabbed dashboards.
Vibe-coding with the LookML Agent: This specialized AI agent and new VS Code extension enable full-lifecycle LookML development, management, and deployment entirely outside of Looker in any VS Code-based IDE. The agent accelerates semantic modeling by translating natural language into LookML, generating models directly from existing BigQuery/AlloyDB datasets, and integrating easily with existing agentic skills. Allowing developers to develop and interact with Looker within a single interface delivers a highly cohesive and accelerated environment.
Universal semantic layer: With the new in-database analytics model support, LookML can support graph models and complex semantic ontologies with BigQuery Graph and Snowflake Semantic Views. LookML can flex to a broad set of governed use cases for data agents for industries like retail, supply chain, cybersecurity, with graph relationships alongside table-based models for scale-out BI agents.

By combining Looker's analytical governance with Google's Agentic Data Cloud, we help ensure your autonomous AI agents operate on verified enterprise metrics, not hallucinated guesswork. Whether you are deeply embedded in the Google Cloud ecosystem or leveraging Looker across a multi-cloud data architecture, Looker provides the openness, scale, and semantic grounding required to power the future of business.

Download the report: Read the full 2026 Gartner Magic Quadrant for ABI Platforms to explore the detailed vendor evaluations.
Explore agentic Looker: Read our full recap of Looker Updates for Agentic BI at Next '26.

_{Gartner Magic Quadrant for Analytics and Business Intelligence Platforms - Anirudh Ganeshan, Edgar Macari, Christopher Long, June 29, 2026}

_{GARTNER is a registered trademark and service mark of Gartner and Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.}

_{This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Google.}

_{Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.}

Introducing new Explores and Merge Queries in Looker

Tue, 16 Jun 2026 16:00:00 +0000

A key goal for many enterprises in the AI era is to empower their employees to uncover actionable data insights on their own. To help, we are evolving Looker Explore with a streamlined interface and integrated AI, so every usey can confidently turn data into a clear path to action.

A team of AI assistants

At the heart of the new Explore release is a suite of AI capabilities that guides users from their very first click with new insight and expression assistants.

AI-assisted Quick Start

We are virtually eliminating the cold start from an empty canvas. If the data modeler hasn't built predefined Quick Starts, Looker automatically generates a query for the user, tapping into Google’s latest Gemini models to generate ad hoc Quick Starts that can help users dive deep into the data, beyond visible fields, and surface potential questions the data can tackle.

The new Explore interface in Looker

Insight Assistant

Users can now prompt Looker Explores in natural language to modify data tables and visualizations. The Insight Assistant uses the Conversational Analytics API to identify relevant fields, apply filters, sort data, and construct the data table. We expect this feature to be a significant time-saver that can provide a rapid starting point for complex analysis.

You can ask questions in natural language to update data tables in Looker

Expression Assistant

Users can also use natural language to describe their custom calculation, and Looker will automatically fill in the appropriate syntax, without having to learn Looker Expression (Lexp) syntax. Users can also re-prompt the assistant to iterate on custom field expressions.

AI-generated Explore summary

If a user-generated description does not exist for an Explore, Looker will provide an AI-generated summary, to help data analysts rapidly gain familiarity.

An intuitive, modernized UI

In addition to these new assistants, we’ve updated the Looker user interface to be more modern and polished. There, you’ll find:

A customizable workspace: The new interface features a resizable field picker pane, with more easily readable long field names.
Data table contextual menus: Looker now offers powerful functionality right in the data table. Users can access quick menus on columns to switch data granularities, apply filters like 'IS NOT BLANK' or 'IS NOT NULL', and instantly add complex table calculations like '% of column' or 'running total'.
Visual pivots: Users will soon be able to drag and drop fields into a panel to pivot data into columns, rows, and aggregated values.

Connect data with redesigned merge queries

Looker’s new interface to quickly join modeled data

In addition, we redesigned Looker Explore’s Merge Query workflow with a unified, in-window architecture that includes:

A dynamic three-panel interface: The new design maintains context beautifully by displaying three simultaneous panels: a "configure joins" list on the left, a dynamic field picker in the middle, and your data preview/visualization on the right. You can edit a source query without losing the context of the overarching join configuration.
Smart join suggestions: The new panel automatically suggests optimal join fields, such as state and month, and shows the combined fields.
Instant query linking: If you have an existing query you want to use, you can paste a prebuilt query URL to start a join.
Expanded row limits: We've increased the default row limit for non-BigQuery sources to 50,000 rows.

By pairing conversational AI with a dramatically simplified user interface, Looker’s new Explore experience gives your business users the tools they need to investigate their data with confidence. Reach out to your Looker administrator today to enable this feature. For more information, click here for detailed documentation.

What’s new in data agents: Supercharging your AI workflows

Mon, 15 Jun 2026 17:00:00 +0000

The rise of AI agents is fundamentally disrupting applications and analytical systems. Generic AI platforms don't usually have access to the context stored within enterprise databases. This is because traditional data architectures often lack context for agents across the data estate, which can lead to agents being inaccurate. They’re also prone to security gaps due to a lack of granular access controls.

Google’s Agentic Data Cloud is an AI-native system of action that includes both operational and analytical systems. By infusing AI across the entire stack — from custom silicon to frontier Gemini models — we provide a deterministic, template-driven developer framework that allows agents to ground their reasoning in real-time enterprise data with near-100% accuracy, as well as unified governance.

Today, we’re making it easier to develop agents, with a whole host of new data agents and tools: for business analysts within Conversational Analytics; for data scientists, engineers, and database admins with a series of Google-built Data Agents that provide greater automation and intelligence; and finally, for developers, with Data Agent tools that help you better integrate with today’s open agentic ecosystem.

1. Conversational Analytics

To support developers building agents using natural language, we’re announcing expanded support for Conversational Analytics across Data Cloud.

Conversational Analytics in BigQuery integrates a sophisticated AI reasoning engine directly into BigQuery Studio, helping data and business teams go beyond writing manual SQL, leveraging business context to ground answers using multimodal synthesis and deep-dive research. Agentic workflows, in preview for select customers, automate root-cause analysis, and schedule actions — turning enterprise data into proactive, actionable intelligence.

Create agents for faster data insights with Conversational Analytics in BigQuery

Conversational Analytics in Lakehouse, now in preview, extends the Lakehouse unified infrastructure, so users can query distributed data lakes across AWS, Azure, and Google Cloud using natural language. This makes it possible to combine insights across cloud platforms without moving a single byte of data.

Conversational Analytics in AlloyDB, Spanner, and Cloud SQL, now in preview, supports out-of-the-box conversational AI, making data accessible for everyone. AlloyDB, Spanner, and Cloud SQL users can start natural-language conversations with their databases to gain visibility into their real-time operational data and capture analytical insights.

Use Conversational Analytics to get answers from your operational data

Looker Embedded Conversational Analytics, now generally available, allows you to embed agents directly into your custom applications and internal workflows via a low-code iframe implementation, making it easier to ship production-ready, conversational AI within any application. Additionally, with the Conversational Analytics API in Looker, you can create multi-turn conversational workflows that offer AI-powered recommendations, while also verifying and explaining the underlying SQL query. We are also significantly upgrading Looker’s core Conversational Analytics agent, which is already GA, with superior reasoning and semantic grounding, helping to eliminate ambiguity.

Embed agents directly into your applications for conversational AI

2. New data agents

To help data professionals move from reactive data management to proactive intelligence, and business analysts better interact with their dashboards, we’re announcing a new set of data agents that bring automation, intelligence, and natural language capabilities into their daily workflows.

Data Engineering Agent, now generally available, automates the heavy lifting of building and maintaining data pipelines. It transforms natural language requirements into optimized SQL or Python code for BigQuery and Dataflow, while proactively identifying and fixing pipeline breaks. By suggesting schema improvements and partitioning strategies, it ensures your data foundation is scalable, reliable, and performance-tuned without manual trial and error.

Data Science Agent accelerates the path from raw data to production-ready models. It assists data scientists by suggesting relevant features, generating boilerplate notebook code, and automating the technical documentation process.

Database Observability Agent, in preview with select Cloud SQL, AlloyDB, Spanner, and Bigtable customers, proactively monitors database performance and continuously identifies potential issues before they escalate. It then delivers intelligent recommendations and multi-turn remediation workflows for fast, comprehensive troubleshooting and optimization. It provides performance analytics for the entire database fleet, helping you quickly identify performance optimization opportunities across databases.
Database Onboarding Agent, in preview with select customers, takes the guesswork out of database selection and deployment. By evaluating your stated requirements — from simple use case descriptions, to complex enterprise needs — it recommends the best Google Cloud database and guides you through provisioning.
Looker Dashboard Agent, now in preview, enables conversational interaction with data within dashboards. Users can ask natural language questions and receive context-aware answers within the dashboard. This feature also provides AI-generated summaries that highlight key takeaways and insights from the dashboard.
Conversational Analytics in Gemini Enterprise, now in preview for Looker, BigQuery, and Lakehouse, brings governed intelligence built by data practitioners directly to business leaders. It serves as a "front door" to the Google Data Cloud, allowing business users to consume agents built in BigQuery, Looker, or Lakehouse without needing to access technical consoles. By publishing these agents from Google Data to Gemini Enterprise, organizations provide a single, grounded interface for precision data exploration and immediate answers to the business users.

Deep Research Agent, now in preview, uses the Knowledge Catalog to solve high-stakes, multi-layered business problems. It moves beyond simple search to build comprehensive research plans that synthesize intelligence from internal documents, BigQuery tables, and the public web. The result is a detailed report with dynamic visualizations and verifiable citations, that respect enterprise privacy and user permissions all the while.

3. Tools for data agents

Open-source standards for agentic development provide developers building AI applications and custom agents with a unified framework to access data and tools consistently and securely. Today, we are announcing the following tools to help ground your agentic development initiatives:

Data Agent Kit: now in preview, provides a standardized suite of skills and tools directly within preferred developer environments (IDE/CLI), empowering data practitioners to discover, transform, and action data at scale using the prescriptive guidance from the Agentic Data Cloud capabilities.
Managed MCP Servers for Databases, now generally available for AlloyDB, Spanner, Cloud SQL, Bigtable, and Firestore, fully manages the infrastructure required to connect AI models securely to your data, so you don’t have to host, secure, or scale MCP servers yourself. Now, developers can provide their agents with up-to-date context from across our database portfolio, so that your AI models can reason and act upon your most up-to-date enterprise data.
Managed MCP Server for Looker, now in preview, allows any MCP client or agent platform to query Looker's semantic models, extending governed BI insights across third-party applications.

Access Looker semantic models through Managed MCP Server

MCP Toolbox for Databases 1.0, now generally available, has achieved a major stability milestone, giving you the confidence to build production applications. We also overhauled the documentation, making the platform significantly more approachable for both human developers and autonomous agents.
QueryData for Cloud SQL, AlloyDB, and Spanner, now in preview, turns natural language questions into database queries. It’s built natively into these databases, and provides near-100% accuracy for natural language to SQL conversions through metadata, query examples, and evals.
Universal Commerce Protocol (UCP) Analytics powered by BigQuery, now in preview, enables merchants and developers to stream real-time events from UCP directly into BigQuery (see sample). This integration provides out-of-the-box observability for agentic commerce, allowing teams to monitor conversion funnels, track automated checkout performance, and identify system errors. By standardizing these metrics within BigQuery, businesses can bridge the gap between AI-driven transactions and existing business intelligence workflows.

Details on how to access the new agents and tools can be found from each of the documentation links on this page. Data agents are also available through Gemini Enterprise and the Google Cloud console.

Transform dashboards into interactive data experiences with Looker agents

Thu, 11 Jun 2026 16:00:00 +0000

Dashboards have long served as a primary way for organizations to extract insights from data, but they can fall short in agile environments: Dashboards aren’t interactive and don’t allow you to ask follow-up questions. This forces users to step outside their workflows or turn to data analysts to get the answers they need. Today, we are introducing Looker dashboard agents in preview, embedding intelligent, conversational data agents directly within dashboards and empowering users to explore their business intelligence (BI) data using natural language.

Start a conversation with a Looker dashboard agent

Interactive agent-led investigations

Traditionally, dashboards have presented a static view of data. With dashboard agents in Looker, users can explore their data directly within the dashboard interface. Users can start a conversation by clicking the Gemini icon and asking natural-language questions to receive contextual insights.

The accuracy of a data agent depends on the business context it is provided, and its ability to map appropriate metrics and dimensions to users’ inquiries. The Looker dashboard agent has direct context about the user’s applied filters, cross-filters, and pre-curated tiles, helping it to generate highly relevant and accurate answers to complex business questions.

Should a query require more data, the agent can access underlying Explores to uncover additional information. These insights are paired with relevant charts and natural language explanations to simplify data exploration.

Explore data beyond dashboard to uncover deeper insights

Tailor the agent to your business

Data analysts curate dashboards to provide business users with precise perspectives on organizational data. To maintain this kind of consistent and reliable analytical environment, the Looker dashboard agent is highly configurable. Analysts can add context on top of the Looker semantic layer by providing natural-language instructions directly to the agent. This way, they can define exactly how the agent interprets unique business logic and tailors responses for the target audience. By enabling self-serve data analysis, dashboard agents help analyst teams scale to meet the increasing data demands of the business.

Configure Looker dashboard agents

Inherited trust and transparency

For users to adopt an AI-based system, they must trust the information it provides them. When generating an insight, the Looker dashboard agent explicitly shows its work by displaying intermediate reasoning, referenced dashboard tiles, and applied filters. Additionally, the administrator needs to trust users only have access to data and insights to which they are authorized. The dashboard agent is backed by Looker’s governance model, managed through standard permissions.

We are actively working on additional capabilities for the Looker dashboard agent, including support for iframe embedding, allowing organizations to bring dashboard agents alongside Looker dashboards into any essential portal or application.

Enable dashboard agents today

With Looker version 26.08.11 and later, administrators can activate the dashboard agent capability by toggling "Enable Chat with Dashboard" within the Gemini in Looker settings. Once enabled, authorized users will see the Gemini icon and can begin chatting with their dashboard data immediately. Please explore our support documentation for more detailed information.

What’s new in the Agentic Data Cloud: Powering the System of Action

Wed, 22 Apr 2026 12:00:00 +0000

Companies are shifting from gen AI that simply answers questions to autonomous agents that perceive, reason, and act on their behalf. Attempting to scale these agents on legacy stacks exposes structural failures that can lead to fractured governance, a persistent trust gap, and broken reasoning loops, all while causing costs to spiral.

To solve this, we’re introducing the Agentic Data Cloud: an AI-native architecture that evolves the enterprise data platform from a static repository into a dynamic reasoning engine. It closes the gap between thinking and doing, allowing AI agents to act on your business data and context. While last-generation systems of intelligence were built only for human scale, the Agentic Data Cloud is a System of Action, built for agent scale.

Leading organizations are already using the Agentic Data Cloud to deliver tangible value:

Vodafone has launched hundreds of agents to deliver uninterrupted service to their customers, which is expected to save them millions of euros every year.
American Express is moving a core on-premises data warehouse and hundreds of production applications to BigQuery to power trusted agentic commerce at scale.
Virgin Voyages is using over 1,000 specialized AI agents, including one that slashes mass itinerary rebooking from six hours to just 11 minutes.

Today, we’re announcing three new innovation areas powering our Agentic Data Cloud:

A universal context engine that provides agents with trusted business context to drive higher accuracy.
Agentic-first practitioner experiences to evolve the role of data practitioners and developers as orchestrators of agents.
An AI-native, cross-cloud lakehouse that eliminates data silos by connecting your entire data estate.

Enabling agents with a universal context engine

AI is only as smart as its context. If an agent doesn't understand your definition of "margin" or the intricate relationships in your supply chain, it’s forced to guess. In the age of the agentic enterprise, data alone is not enough, and the old model of governance is insufficient. This is why we evolved the Dataplex Universal Catalog into the Knowledge Catalog, which maps and infers business meaning across your entire data estate, using a rigorous framework of aggregation, continuous enrichment, and search.

Here’s how it works:

Aggregation: To build true context, you must bring it together from everywhere. We are aggregating native context across your Google Cloud and partner data platforms. This includes third-party catalogs, applications, operating systems, and AI platforms like Palantir, Salesforce Data360, SAP, ServiceNow, and Workday (Preview). By using our Lakehouse, your third-party data assets are automatically mapped to the Knowledge Catalog. For Google Cloud sources, we are automating business logic with the new LookML Agent (Preview) and BigQuery measures (Preview), which embeds that business logic natively into the platform.
Continuous enrichment: The Knowledge Catalog delivers continuous enrichment by analyzing usage logs across your organization and profiling data behind the scenes. It learns how your enterprise actually uses data, not just what it is. This extends to unstructured data. The moment a file lands in Google Cloud Storage, our Smart Storage (Preview) instantly tags and enriches images and, soon, PDF objects. The Knowledge Catalog also identifies useful collections of unstructured data and uses Gemini to automatically generate missing schemas, mapping complex relationships so your AI is no longer flying blind.
Search and retrieval: Creating a massive context layer is great, but in the agentic era, search has evolved to be the new query path. The hardest problems at enterprise scale are speed, relevance, global reach, and security. To solve this, the Knowledge Catalog uses a sophisticated hybrid search stack built on Google Search innovation. To deliver relevance, it combines semantic and lexical matching with intelligent, machine-learning-based re-ranking. To deliver trust, we are enforcing your security permissions natively with access-control-aware search, so agents can only retrieve and act on the assets they are authorized to see. This high-precision infrastructure instantly identifies trusted context and feeds it to specialized agents.

The Knowledge Catalog now powers the Deep Research Agent (Preview). Part of the suite of Google-made agents available in Gemini Enterprise, this agent can perform multi-step reasoning across Google Cloud data platforms, such as BigQuery, as well as internal documents and web assets to answer complex questions with citations and precision that previously required weeks of manual effort.

Agentic-first practitioner experiences

As we move to this new architecture, the data practitioner role shifts from writing manual pipelines to orchestrating intent-driven engineering.

We’re accelerating this transition with the Google Cloud Data Agent Kit (Preview). Rather than introducing a new interface, we are launching a portable suite of skills, tools, environment-specific extensions, and built-in plugins, that drop into the environments developers love. By meeting practitioners where they already build — including VS Code, Gemini CLI, Codex, and Claude Code — we turn your IDE, notebook, or agentic terminal into a native data environment. This enables your environment to autonomously orchestrate a wide range of business outcomes, automatically selecting the right frameworks (e.g., dbt, Apache Spark, or Apache Airflow) and generating production-ready code based on Google’s gold standards.

This kit doesn't just connect tools — it injects high-performance capabilities directly into the developer's flow, scaling to petabytes without moving data. In fact, the Data Agent Kit features the same skills and tools that power our own out-of-the-box agents, including:

Data Engineering Agent (GA): Builds complex pipeline transformations from scratch and enforces governance rules to keep bad data out of production.
Data Science Agent (GA): Automates the model lifecycle — from wrangling to training — scaling across BigQuery Dataframes and Serverless Apache Spark.
Database Observability Agent (Preview): Acts as a 24/7 guardian for your infrastructure, diagnosing root causes and executing database remediations.

To help ensure the smooth execution of agents, Google Cloud has fully embraced Model Context Protocol (MCP), which provides a secure, universal interface that allows any agent to discover and use your data assets across our core engines, including: BigQuery, Spanner (Preview), AlloyDB, Cloud SQL (GA), and Looker MCP (Preview). MCP for Google Cloud uses our security stack, governing agent interactions based on your existing IAM policies, VPC Service Controls, and data residency requirements.

We’re also reimagining the business user experience with Conversational Analytics, now supported across BigQuery (GA), Cloud SQL, Spanner, AlloyDB (Preview), and Looker (GA). Organizations can simply publish these custom analytical agents in Gemini Enterprise, enabling employees to chat with live data in a familiar interface. By removing the technical barriers, we’ve eliminated the weeks spent waiting for manual reports, allowing businesses to move at the speed of thought.

A cross-cloud foundation built for agentic scale

For an agent to act, it must have a fundamentally open foundation. If an agent is blocked by cross-cloud latency or trapped in a proprietary walled garden, its autonomy is broken. That’s why we are introducing a truly borderless, cross-cloud Lakehouse that liberates your data wherever it resides by:

Connecting analytical estates: We’re integrating Cross-Cloud Interconnect (CCI) directly into our data plane. By combining CCI’s dedicated, high-speed private networking with Apache Iceberg REST Catalog, we’re enabling connectivity across clouds that is low latency and eliminates massive egress fees. As a result, agents can use data across AWS and Azure as if it were local to Google Cloud with seamless cross-cloud access.
Ending proprietary silos: We’re championing open federation to end the era of proprietary catalogs by launching bi-directional federation (Preview). Powered by the Iceberg REST Catalog, engines can now read directly from Databricks Unity Catalog on Amazon S3 (Preview), Snowflake Polaris (Preview), and the AWS Glue Data Catalog on Amazon S3 (Preview). This is reinforced by enhanced Lakehouse Governance (Preview), which ensures your security policies and access controls apply instantly across this borderless environment.
Unlocking operational data: We’re announcing Spanner Omni (Preview), unchaining the most scalable, globally consistent database on the planet. For the first time, you can run the Spanner engine anywhere — across clouds, on-premises, or on your laptop — with the same capabilities used to run Google.
Bridging the insight to action gap: We’re also closing the gap between insight and action. Most "unified" data platforms force the creation of complex ETL pipelines that block agents from accessing your real-time data. With Lakehouse federation for AlloyDB (Preview), we’re removing these pipelines by providing protocol-level, zero-ETL synchronization to give agents access to deep analytical history with low latency in operational transactions.

Automating the future

Moving to agent scale generates orders of magnitude more workloads. To support this, we are announcing four major performance breakthroughs:

Lightning Engine for Apache Spark delivers up to 2x the price-performance over the proprietary market alternative.
Managed Lustre delivers up to 10 terabytes-per-second of throughput to make sure data moves quickly enough for demanding models.
Bigtable now supports an in-memory tier that delivers sub-millisecond read latency for real-time applications. This means you can finally eliminate separate, side-by-side caching layers.
BigQuery fluid scaling helps lower costs by up to 34% on average for autoscaling workloads, scaling up resources instantly when agents act, and scaling back when they don't.

Build your success on a System of Action

The era of passive observation is over. The future belongs to the System of Action, made possible by Google’s Agentic Data Cloud.

Ready to see what an Agentic Data Cloud can do for your business?
Sign up for a strategy workshop today on how to get your data ready to fuel autonomous agents through a System of Action.

Want to learn more about an Agentic Data Cloud?
Read our blueprint for turning passive data into proactive action.

Welcome to the agentic BI era with Looker

Wed, 22 Apr 2026 12:00:00 +0000

By combining the analytical depth of Looker with Google’s Agentic Data Cloud, the potential to transform how we model, interact with, and act on our data appears limitless. This week at Google Cloud Next, we are thrilled to share how we are reimagining the business intelligence (BI) stack with Looker for the AI era, starting with a deeper integration with BigQuery.

Our product strategy is focused on delivering trusted data and prescriptive insights for everyone to take actions through Agentic BI.

We’ve rebuilt Looker with Gemini to power a new era of AI agents. With Looker providing the trusted data foundation, your team can innovate rapidly without losing accuracy.

Conversational BI and agents: From conversations to actions

At Next ‘26, we are announcing Looker BI Agents that don’t just provide static answers, but trigger downstream business actions grounded in the Looker semantic layer and your existing enterprise governance framework.

Today, we introduced a number of new agents in Looker:

Dashboard Agents - getting answers to your data directly within a dashboard.

Conversational Agents upgrades (Preview): We are significantly upgrading our core Conversational Analytics agent, which is already GA, with superior reasoning and semantic grounding, helping to eliminate ambiguity. New visibility tools provide admins with end-to-end observability, so that they can monitor performance trends and refine model accuracy at scale. Teams can now also publish Conversational Analytics in Looker directly into Gemini Enterprise to incorporate trusted data into broader workflows.
Dashboard Agents (Preview): Dashboard Agents bring conversational capabilities directly into your BI workflows, providing instant summaries and the ability to ask questions directly within a dashboard.
Embedded Conversational Experiences (GA): You can now embed Conversational Analytics agents directly into your custom applications and internal workflows, allowing users to query data via the UI or API without leaving their specialized tools.
Agentic Workflows in Looker (Preview): Agents can now monitor critical metrics for irregularities and identify hidden correlations and "what’s next" recommendations, so you can address shifts in the business — before they impact the bottom line.

“At YouTube, we're focused on helping creators succeed and bring their creativity to the world. We've been testing Conversational Analytics in Looker to give our partner managers instant, actionable data that lets them quickly guide creators and optimize creator support." - Thomas Seyller, Senior Director, Technology & Insights, YouTube Business

Reimagining self-service: AI power, UI control

AI powered Google-easy exploration for ease and simplicity.

We are making Looker more intuitive and easy for both business users and analysts.

Additionally, we are introducing new AI assistants in Looker Explores to erase the hard parts of BI and transform complex workflows into natural language interactions that feel as familiar as a Google search. New today are:

The new and modernized Looker interface (Preview): A modernized, Google-easy drag-and-drop interface for AI-driven self-service Explores, featuring tabbed dashboards (GA) and paginated reporting (GA).
Visualization Assistant (GA): Use natural language to create and refine charts. Simply ask to change drafted visuals to a stacked bar chart or color-code by region, and Gemini handles the rest.
Expression Assistant (Preview): Generate custom dimensions, measures, and filters just by describing your desired logic — no more having to remember syntax!
Insight Assistant (Preview): Beyond just showing data, Insight Assistant automatically generates summaries and highlights key trends within your reports, helping you identify the so what in seconds.
Self-service Explores (GA): Blend personal data from CSV and Excel files with your enterprise data, while a new interface makes it easier to build models visually. Behind the scenes, Looker handles the LookML code. Together, they provide the agility of a spreadsheet with the rigorous version control of an enterprise platform, so that even ad-hoc data analysis remains governed.

“Our users (or customers) love the flexibility of Excel but fear the lack of governance. Looker’s Self-service Explores give them the best of both worlds. They can upload ad-hoc CSVs for a quick 'what-if' analysis, but because that data is analyzed alongside our LookML dimensions, we know the underlying business logic is still accurate.” - John Pettit, Chief Technology Officer, Promevo

A trusted and open platform

Having a rock-solid, governed semantic layer is a strong way to prevent AI hallucinations and ensure a single source of truth, and we have some exciting announcements in this area:

Open BI and MCP (Preview): Looker supports open-source Model Context Protocol (MCP) via MCP Toolbox, and now we’re offering an all new managed MCP server that’s native to Looker, for ease of customer management. Through this open BI approach you have the flexibility to choose how you deliver AI transformation to your users.
Looker Extension for VS Code (Preview): We are accelerating the developer lifecycle with a new intelligent setup wizard and a specialized LookML AI Agent that translates business intent directly into production-ready code. This new plugin for VS Code and Agentic IDEs lets you vibe-code with LookML, delivering an AI-powered authoring experience for building LookML models simply by describing your desired outcome.

Updated LookML with Gemini support in the Gemini-powered IDEs

Continuous Integration (CI/CD) (GA): Now generally available, this fully integrated advanced CI capability in Looker automates SQL validation and content testing within your development workflow. By identifying potentially breaking changes before they reach production, Looker helps ensure model updates are reliable and accurate.
Knowledge Catalog integration (Preview): With Knowledge Catalog, Looker transforms metadata into a semantic graph, providing the essential context AI agents need to complete tasks autonomously.

The future of data isn't just about seeing insights — it's about acting on them. By combining the power of Gemini with Looker’s trustworthy semantic layer, we are making data-driven actions a reality for every user, in every workflow, every day.

Welcome to the era of Agentic BI.

Get started in your journey to transform your business through the agentic era with Looker at cloud.google.com/looker or start your developer experience at https://docs.cloud.google.com/looker/docs/api-sdk.

Data Studio returns as new home for Data Cloud assets

Fri, 10 Apr 2026 16:00:00 +0000

In today's data-rich environment, organizations possess vast amounts of information. Yet, bridging the gap between that data and the users who need to make daily, informed decisions remains a challenge. Users need a single place to curate and analyze their data from the many different sources that impact their business each day.

We are sharing the next step in our mission to solve this challenge and reintroducing a beloved and familiar name, Data Studio (formerly Looker Studio).

In addition to its powerful data visualization capabilities, Data Studio is playing a significant role in the AI era serving Google Data Cloud content. With Data Studio, you have a single place to browse and interact with a variety of Google data sources and assets — from Data Studio reports, to BigQuery conversational agents, to data apps built in Colab notebooks.

Data Studio: reports, data apps, and conversational agents in one place

Extending our vision for analytics in the AI era

Since bringing Data Studio to the Google Cloud family five years ago, customers have continued to innovate with Data Studio as a place to visualize and share their data assets. Meanwhile, as AI becomes a critical component of practically every business, we’ve heard from our customers that they need a single place to save, organize and browse their data assets.

As part of this reintroduction, with Looker as our enterprise business intelligence platform, we are evolving Data Studio to complement the Looker platform, independently. As we have redesigned Data Studio, Looker has also recently seen significant investments in its self-service and visualization offerings, including agentic capabilities for use cases that demand trusted, governed data powered by a central semantic model.

We believe the new Data Studio is the ideal choice for personal data exploration — a place to craft ad-hoc reports, and quickly visualize data across Google’s ecosystem, from BigQuery to Google Sheets and Ads. This strategic differentiation ensures customers have the right tool for the right job.

Two flavors: Data Studio and Data Studio Pro

The new Data Studio experience is available in two editions.

Data Studio continues to offer powerful, no-cost individual analysis and visualization, serving as the on-ramp for creating and sharing ad-hoc reports, transforming data to an interactive dashboard in minutes.
Data Studio Pro is designed for scaling teams and organizations that need more agility and control, including AI features and deep integration with Google Cloud for enterprise-grade security, management, and compliance capabilities. Pro licenses can be purchased directly from the Google Cloud console or the Google Workspace Admin Console.

Upgrading to the new Data Studio should be largely transparent for the many users who count on this product in their daily work. All existing reports, data sources, assets and users will be transitioned to the new experience with no action on your part. Learn more about what’s coming to Data Studio and our vision for Data Cloud and Analytics at Google Cloud Next ‘26 later this month.

Introducing Looker self-service Explores for faster ad-hoc analysis

Mon, 06 Apr 2026 16:00:00 +0000

By design, Looker is the enterprise semantic platform which ensures that every data set meets a high standard of accuracy by acting as a single source of truth and providing long-term consistency of your metrics. Today, we are introducing a complement to this governed framework: self-service Explores, to accelerate high-velocity, ad-hoc analysis. Self-service Explores allows you to bring your own data directly into the Looker semantic layer, providing instant access to insights while maintaining the integrity of your existing governed data ecosystem.

Data teams often find themselves caught between two worlds. On one side, there’s the trusted, governed world of modern BI, where every metric is defined and every row is verified. Then there’s the agile, anything goes nature of spreadsheets and CSV files where you can get answers in seconds but run the risk of ending up in a siloed data vacuum.

Self-service Explores bring the value of modern, governed BI to the experimentation self-starting capability of spreadsheets, allowing anyone with the right permissions to turn a flat file into a fully functional Looker Explore in seconds. You can also import from Cloud from Google Drive and quickly transform Google Sheets data into conversational analytics. No code, no waiting — just insights.

You can drag and drop a comma separated or spreadsheet file (.csv, .xls, or .xlsx) or pull directly from Google Sheets, and Looker automatically creates an Explore. Behind the scenes, these files are securely stored in your own BigQuery instance, ensuring your data remains within your controlled infrastructure.

From importing a CSV to a self-service Explore in seconds

Key capabilities to power your analysis

With Looker self-service Explores, you gain:

Instant file uploads: Use a simple drag-and-drop interface to ingest local files for one-off analyses or testing a hypothesis before committing it to a permanent model.
Connect directly to Google Sheets: Easily import data via Google Sheets either via Oauth or by specifying the Google Sheets URL and sharing the document.
Merge queries (BigQuery): You can now combine your uploaded local files with standard, modeled Looker data, allowing you to enrich official company metrics with external data points to find new correlations. We’ve also added enhanced merge queries capabilities so you can perform merges on unlimited data as long as the data is on the same BigQuery connection.
Re-upload and refresh: You can easily re-import or update files within existing self-service Explores to keep your ad-hoc dashboards current.

Exploration through conversation, with red-tape free governance

Business intelligence in the agentic era has created new opportunities for business and data leaders throughout your organization to engage with their information faster and more intuitively, without significant technical demand. Self-service Explores bring support for conversational analytics, enabling you to ask questions in natural language about your uploaded data and get immediate answers, outlined visually, with grounding in Looker’s semantic layer. Users can chat with their data to drill down deeper, gaining more insights with follow-up questions and refinement.

Additionally, self-service Explores are built with admin controls at the forefront. Admins have full visibility and monitoring capabilities, with clear distinctions between ad-hoc data and modeled data. This gives users the freedom to explore, while maintaining the integrity of your core business logic. Self-service Explores give you the agility of a spreadsheet with the scale and security of BigQuery. It’s about spending less time waiting for a data model and more time actually using your data. Get started today.

Conversational Analytics now available for Looker Embedded environments

Mon, 06 Apr 2026 16:00:00 +0000

Looker Embedded analytics are at the heart of many next-generation data products, enabling monetization with live metrics and customizable user experiences. In the AI era, users expect apps to be highly interactive and conversational, and for data to be contextual, accessible and intuitive. Today, we are delivering conversational analytics in Looker Embedded environments with the Conversational Analytics API, now generally available, extending the natural language experience users have grown to expect from Looker to more surfaces, limited only by customers’ imagination.

Leading the shift to composable and agentic BI

Organizations are building integrated data experiences that meet users where they work. This is happening alongside the rise of agentic IDEs, where developers use AI agents to plan, code, and execute complex engineering tasks. Leveraging Looker’s embedded architecture within these environments incurs many benefits:

Differentiated agent experiences: Build high demand, unique conversational experiences that set your products apart.
Accelerated data monetization and developer extensibility: Turn raw data into sophisticated embedded products, transforming data assets into high-margin revenue streams.
AI-readiness in the IDE: Build with agentic IDEs with confidence, using Looker’s semantic layer to reduce hallucinations and maximize speed-to-market.

Conversational Analytics generally available for embedded users

Building on Looker’s existing embedded framework and governed semantic layer, you can now integrate Gemini-powered natural language querying and AI recommendations via a low-code iframe implementation or our extensible SDKs. This makes it easier to ship production-ready, conversational AI within any application.

With native support for querying multiple Looker Explores, a built-in code interpreter, and customizable theming, you can now provide a private-labeled, high-reasoning agent experience alongside your core features, bridging the gap between complex data models and intuitive user discovery.

Embed Conversational Analytics in Looker anywhere in your application using an iframe

Conversational Analytics now available from the Looker API

With our Conversational Analytics APIs, you can now build conversational experiences backed by Looker Explores directly within your customer-facing applications. By leveraging the Looker API, you can create multi-turn conversational workflows that offer AI-powered recommendations, while also verifying and explaining the underlying SQL query. The Looker API turns to the Looker SDK for user authentication and management, making it easy to integrate agentic conversations anywhere in your app.

Building a custom UI with Conversational Analytics in Looker API endpoints

Data exploration is a conversation away

The shift from static and inflexible dashboards to AI-driven exploration is transforming how businesses deliver value to their customers, empowering them to do more. Grounding these new conversational capabilities in Looker’s semantic layer means the insight accuracy you’ve always relied on from Looker is now available in third party applications, so you can know the insights are verifiable.

Whether you use the embedded iframe option or the Looker SDK, you can now build data experiences that don't just show information, but engage users in a dialogue. To start building conversational experiences in your own products with Looker Embedded, check out the documentation and best practices guide.

New in Looker: self-service Explores, tabbed dashboards, and custom themes

Fri, 19 Dec 2025 17:00:00 +0000

Data teams seem to be constantly balancing the need for governed, trusted metrics with business needs for agility and ad-hoc analysis. To help bridge the gap between managed reporting and rapid data exploration, we are introducing several new features in Looker, to expand users’ self-service capabilities. These updates allow individuals to analyze local data alongside governed models, organize complex dashboards more effectively, and align the look and feel of their analytics with corporate branding, all within the Looker platform.

Analyze ad-hoc data with Looker self-service Explores

Valuable data often exists outside of the primary database — whether in budget spreadsheets, sales lists, or ad-hoc research files. With self-service Explores, now in Preview, users can upload CSV and spreadsheet-based data using a drag-and-drop interface directly within Looker.

This feature allows users to combine local files with fully modeled Looker data to test new theories and enrich insights. Once uploaded, users can visually add new measures and dimensions to their self-service Explores, customize them, and share the results via dashboards and Looks.

Uploading a CSV file and creating a new self-service Explore in just a few clicks

To maintain governance, administrators retain oversight regarding which files are uploaded to the Looker instance and who has permission to perform uploads. Additionally, we have introduced a new content certification flow, which makes it easier to signal which content is the vetted, trusted source of truth, ensuring users can distinguish between ad-hoc experiments and certified data.

Certifying a self-service Explore

Upload data and content certification are available in Public Preview as of Looker 25.20.

Deliver clearer, cohesive data stories with tabbed dashboards

The new tabbed dashboard feature helps dashboard editors organize complex information into logical narratives, moving away from dense, single-page views. Editors can now streamline content creation with controls for adding, renaming, and reordering tabs.

For the viewer, the experience is designed to be seamless. Filters automatically pass values across the entire dashboard, while each tab displays only the filters relevant to the current view, reducing visual clutter. Users can share unique URLs for specific tabs and schedule or download the comprehensive multi-tab dashboard as a single PDF document.

Navigating between tabs on a multi-tab dashboard

This feature is currently available in preview.

Apply custom styling to dashboards

Matching internal dashboards to company branding can help create a familiar data experience and increase user engagement. We are announcing the Public Preview of internal dashboard theming, which allows creators to apply custom changes to tile styles, colors, fonts, and formatting directly to dashboards consumed inside the Looker application.

Applying custom theming for internal dashboards

With this feature, you can save, share, and apply pre-configured themes to ensure consistency. Users with permission to manage internal themes can create new templates for existing dashboards or select a default theme to apply across the entire instance.

You can enable Internal dashboard theming today on the Admin > Labs page.

Enabling the preview for internal dashboard theming

Get started

These new self-service capabilities in Looker are designed to help you and all users in your organization get more value out of your data by improving presentation flexibility and quality. Try self-service Explores and internal dashboard themes for yourself today and let us know your feedback.

How to connect Looker to Gemini Enterprise in minutes, with MCP Toolbox and ADK

Fri, 12 Dec 2025 17:00:00 +0000

We can all agree that the quality of AI-driven answers relies on the consistency of the underlying data. But AI models, while powerful, lack business context out of the box. As more organizations ask questions of their data using natural language, it is increasingly important to unify business measures and dimensions, ensure consistency company-wide. If you want trustworthy AI, what you need is a semantic layer that acts as the single source of truth for business metrics.

But how do you make that data accessible and actionable for your end users? Building off the recent introduction of Looker’s Model Context Protocol (MCP) server, in this blog we take you through the process of creating an Agent Development Kit (ADK) agent that is connected to Looker via the MCP Toolbox for Databases and exposing it within Gemini Enterprise. Let’s get started.

Step 1 - Set up Looker Integration in MCP Toolbox

MCP Toolbox for Databases is a central open-source server that hosts and manages toolsets, enabling agentic applications to leverage Looker’s capabilities without working directly with the platform. Instead of managing tool logic and authentication themselves, agents act as MCP clients and request tools from the Toolbox. The MCP Toolbox handles all the underlying complexities, including secure connections to Looker, authentication and query execution.

The MCP Toolbox for Databases natively supports Looker’s pre-built toolset. To access these tools, follow the below steps:

Connect to Cloud Shell. Check that you're already authenticated, and that the project is set to your project ID using the following command:

code_block: <ListValue: [StructValue([('code', 'gcloud auth list'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff7258d0110>)])]>

Run the following command in Cloud Shell to confirm that the gcloud command knows about your project:

code_block: <ListValue: [StructValue([('code', 'gcloud config list project'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff725f3f190>)])]>

Create a folder named mcp-toolbox:

code_block: <ListValue: [StructValue([('code', 'mkdir mcp-toolbox'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff725f3fc90>)])]>

Go to the mcp-toolbox folder via the command shown below:

code_block: <ListValue: [StructValue([('code', 'cd mcp-toolbox'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff725f3fe50>)])]>

Install the binary version of the MCP Toolbox for Databases via the script given below. This command is for Linux; if you run on Macintosh or Windows, ensure that you download the correct binary. Check out the releases page for your Operation System and Architecture and download the correct binary.

code_block: <ListValue: [StructValue([('code', 'export OS="linux/amd64" # one of linux/amd64, darwin/arm64, darwin/amd64, or windows/amd64\r\ncurl -O https://storage.googleapis.com/genai-toolbox/v0.12.0/$OS/toolbox\r\nchmod +x toolbox'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff7252d1710>)])]>

Deploy Toolbox to Cloud Run

Next, you’ll need to run MCP Toolbox. The simplest way to do that is on Cloud Run, Google Cloud’s fully managed container application platform. Here’s how:

code_block: <ListValue: [StructValue([('code', 'export PROJECT_ID="YOUR_GOOGLE_CLOUD_PROJECT_ID"'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff7252d0a10>)])]>

Enable relevant APIs if needed:

code_block: <ListValue: [StructValue([('code', 'gcloud services enable run.googleapis.com \\\r\n cloudbuild.googleapis.com \\\r\n artifactregistry.googleapis.com \\\r\n iam.googleapis.com \\\r\n secretmanager.googleapis.com'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff7252d1ed0>)])]>

Create a service account and assign necessary roles:

code_block: <ListValue: [StructValue([('code', 'gcloud iam service-accounts create toolbox-identity\r\n\r\ngcloud projects add-iam-policy-binding $PROJECT_ID \\\r\n --member serviceAccount:toolbox-identity@$PROJECT_ID.iam.gserviceaccount.com \\\r\n --role roles/secretmanager.secretAccessor\r\n\r\ngcloud projects add-iam-policy-binding $PROJECT_ID \\\r\n --member serviceAccount:toolbox-identity@$PROJECT_ID.iam.gserviceaccount.com \\\r\n --role roles/cloudsql.client'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff7252d1190>)])]>

For information on getting your client id and secret, read the documentation.

Create a deploy.env file:

code_block: <ListValue: [StructValue([('code', 'LOOKER_BASE_URL="YOUR_LOOKER_URL"\r\nLOOKER_CLIENT_ID="YOUR_CLIENT_ID"\r\nLOOKER_CLIENT_SECRET="YOUR_CLIENT_SECRET"'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff7252d3e50>)])]>

Export the toolbox config into an image variable:

code_block: <ListValue: [StructValue([('code', 'export IMAGE=us-central1-docker.pkg.dev/database-toolbox/toolbox/toolbox:latest'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff7252d2890>)])]>

Deploy MCP Toolbox to Cloud Run:

code_block: <ListValue: [StructValue([('code', 'gcloud run deploy toolbox \\\r\n --image $IMAGE \\\r\n --env-vars-file=deploy.env \\\r\n --service-account toolbox-identity \\\r\n --region us-central1 \\\r\n --args="--prebuilt=looker","--address=0.0.0.0","--port=8080"'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff726449390>)])]>

The Cloud Run will ask if you want Unauthenticated, select No.

Allow Unauthenticated: N

Step 2: Deploy ADK Agent to Agent Engine

Next, you need to configure Agent Development Kit (ADK), a flexible and modular framework for developing and deploying AI agents. ADK was designed to make agent development feel more like software development, to make it easier for developers to create, deploy, and orchestrate agentic architectures that range from simple tasks to complex workflows. And while ADK is optimized for Gemini and the Google ecosystem, it’s also model-agnostic, deployment-agnostic, and is built for compatibility with other frameworks.

Vertex AI Agent Engine, a part of the Vertex AI Platform, is a set of services that enables developers to deploy, manage, and scale AI agents in production. Agent Engine handles the infrastructure to scale agents in production so you can focus on creating applications.

Open a new terminal tab in Cloud Shell and create a folder named my-agents as follows. You also need to navigate to the my-agents folder.

code_block: <ListValue: [StructValue([('code', 'mkdir my-agents\r\ncd my-agents'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff725378110>)])]>

Now, create a virtual Python environment using venv:

code_block: <ListValue: [StructValue([('code', 'python -m venv .venv'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff725379f90>)])]>

Activate the virtual environment:

code_block: <ListValue: [StructValue([('code', 'source .venv/bin/activate'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff72537b350>)])]>

Install the ADK and the MCP Toolbox for Databases packages along with langchain dependency:

code_block: <ListValue: [StructValue([('code', 'pip install google-adk toolbox-core'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff7253784d0>)])]>

Creating your first Agent Application

Now you’re ready to use adk to create a scaffolding, including folders, environment and basic files, for our Looker Agent Application via the adk create command with an app name looker_app:

code_block: <ListValue: [StructValue([('code', 'adk create looker_app'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff72530f610>)])]>

Follow the steps and select the following:

Gemini model for choosing a model for the root agent
Vertex AI for the backend
Your default Google Project Id and region

code_block: <ListValue: [StructValue([('code', 'Choose a model for the root agent:\r\n1. gemini-2.5-flash-001\r\n2. Other models (fill later)\r\nChoose model (1, 2): 1\r\n\r\n\r\n1. Google AI\r\n2. Vertex AI\r\nChoose a backend (1, 2): 2\r\n\r\nEnter Google Cloud project ID [your_current_project_id]:\r\nEnter Google Cloud region [us-central1]:\r\n\r\nAgent created in /home/romin/looker-app:\r\n- .env\r\n- __init__.py\r\n- agent.py'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff72530d810>)])]>

Observe the folder in which a default template and required files for the Agent have been created.

First up is the .env file:

code_block: <ListValue: [StructValue([('code', 'GOOGLE_GENAI_USE_VERTEXAI=1\r\nGOOGLE_CLOUD_PROJECT=YOUR_GOOGLE_PROJECT_ID\r\nGOOGLE_CLOUD_LOCATION=YOUR_GOOGLE_PROJECT_REGION'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff72530cfd0>)])]>

The values indicate that you will be using Gemini via Vertex AI along with the respective values for the Google Cloud Project Id and location.

Then you have the __init__.py file that marks the folder as a module and has a single statement that imports the agent from the agent.py file:

code_block: <ListValue: [StructValue([('code', 'from . import agent'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff72530fc90>)])]>

Finally, take a look at the agent.py file. The contents can be edited to similar to the example below:

Insert the Cloud Run URL highlighted here (. not the one with the project number in the url).

code_block: <ListValue: [StructValue([('code', 'import os\r\nfrom google.adk.agents import LlmAgent\r\nfrom google.adk.planners.built_in_planner import BuiltInPlanner\r\nfrom google.adk.tools.mcp_tool.mcp_toolset import MCPToolset\r\nfrom google.adk.tools.mcp_tool.mcp_session_manager import SseConnectionParams, StreamableHTTPConnectionParams\r\nfrom google.genai.types import ThinkingConfig\r\nfrom google.auth import compute_engine\r\nimport google.auth.transport.requests\r\nimport google.oauth2.id_token\r\n\r\n# Replace this URL with the correct endpoint for your MCP server.\r\nMCP_SERVER_URL = "YOUR_CLOUD_RUN_URL/mcp"\r\nif not MCP_SERVER_URL:\r\n raise ValueError("The MCP_SERVER_URL is not set.")\r\ndef get_id_token():\r\n """Get an ID token to authenticate with the MCP server."""\r\n target_url = MCP_SERVER_URL\r\n audience = target_url.split(\'/mcp\')[0]\r\n auth_req = google.auth.transport.requests.Request()\r\n id_token = google.oauth2.id_token.fetch_id_token(auth_req, audience)\r\n # Get the ID token.\r\n return id_token\r\n\r\n\r\nroot_agent = LlmAgent(\r\n model=\'gemini-2.5-flash\',\r\n name=\'looker_agent\',\r\n description=\'Agent to answer questions about Looker data.\',\r\n instruction=(\r\n \'You are a helpful agent who can answer user questions about Looker data the user has access to. Use the tools to answer the question. If you are unsure on what model to use, try defaulting to thelook and if you are also unsure on the explore, try order_items if using thelook model\'\r\n ),\r\nplanner=BuiltInPlanner(\r\nthinking_config=ThinkingConfig(include_thoughts=False, thinking_budget=0)\r\n),\r\ntools=[\r\nMCPToolset(\r\nconnection_params=StreamableHTTPConnectionParams(\r\nurl=MCP_SERVER_URL,\r\nheaders={\r\n"Authorization": f"Bearer {get_id_token()}",\r\n}\r\n),\r\nerrlog=None,\r\n# Load all tools from the MCP server at the given URL\r\ntool_filter=None,\r\n)\r\n],\r\n)'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff7252a9d50>)])]>

Create a Cloud Storage bucket if necessary:

code_block: <ListValue: [StructValue([('code', 'gcloud storage buckets create gs://BUCKET_NAME --location=BUCKET_LOCATION'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff725a26d10>)])]>

Ensure you are in the my-agents directory. Update the BUCKET_NAME with the Cloud Storage bucket you want to use:

code_block: <ListValue: [StructValue([('code', 'export PROJECT_ID="YOUR_GOOGLE_CLOUD_PROJECT_ID"\r\nadk deploy agent_engine --project $PROJECT_ID --region us-central1 --staging_bucket gs://BUCKET_NAME --display_name "looker-agent1" looker_app'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff725a267d0>)])]>

NOTE: Ensure you grant the Cloud Run Invoker role to the default Agent Engine Service Account (i.e., service-PROJECT_NUMBER@gcp-sa-aiplatform-re.iam.gserviceaccount.com)

Step 3: Connect to Gemini Enterprise

Now it’s time to create a Gemini Enterprise app (instructions here).

Run the below command with the GCP Project Number, Reasoning Engine resource name output from the ‘deploy agent_engine’ command above, and your Gemini Enterprise Agent ID from the Gemini Enterprise Apps interface:

code_block: <ListValue: [StructValue([('code', 'export PROJECT_NUMBER="YOUR_PROJECT_NUMBER"\r\nexport REASONING_ENGINE="projects/XXXXX/locations/us-central1/reasoningEngines/XXXXX"\r\nexport DISPLAY_NAME="Looker Agent"\r\nexport DESCRIPTION="Looker\'s MCP Capability."\r\nexport TOOL_DESCRIPTION="Looker\'s Query Engine is used to answer Ecommerce questions."\r\nexport AS_APP="YOUR_GEMENI_ENTERPRISE_AGENT_ID"\r\n\r\ncurl -X POST \\\r\n -H "Authorization: Bearer $(gcloud auth print-access-token)" \\\r\n -H "Content-Type: application/json" \\\r\n -H "X-Goog-User-Project: ${PROJECT_NUMBER}" \\\r\nhttps://discoveryengine.googleapis.com/v1alpha/projects/${PROJECT_NUMBER}/locations/global/collections/default_collection/engines/${AS_APP}/assistants/default_assistant/agents \\\r\n -d \'{\r\n "displayName": "\'"${DISPLAY_NAME}"\'",\r\n "description": "\'"${DESCRIPTION}"\'",\r\n "adk_agent_definition": {\r\n "tool_settings": {\r\n "tool_description": "\'"${TOOL_DESCRIPTION}"\'"\r\n },\r\n "provisioned_reasoning_engine": {\r\n "reasoning_engine":\r\n "\'"${REASONING_ENGINE}"\'"\r\n }\r\n }\r\n }\''), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff725a264d0>)])]>

Your Looker data will now be available within your Gemini Enterprise app.

If you don’t have access to this feature, contact your Google Cloud account team.

Querying business data made easier

Connecting Looker's semantic layer to Vertex AI Agent services by way of the ADK and MCP Toolbox is a big win for data accessibility. By exposing your trusted Looker models and Explores in Gemini Enterprise, you empower end-users to query complex business data using natural language. This integration closes the gap between data insights and immediate action, ensuring that your organization's semantic layer is not just a source of passive reports, but an active, conversational, and decision-driving asset.

To get started, connect to the MCP Toolbox for Databases on GitHub and set up Looker integration.

Looker and Looker Conversational Analytics extensions available in the Gemini CLI

Fri, 21 Nov 2025 17:00:00 +0000

The ability to easily access and understand data is essential for modern businesses. The Gemini command line interface (CLI) is an open-source AI agent that provides access to Gemini directly in your terminal, enabling you to interact with Google’s latest AI models directly from the interface you know best. With the release of Looker and Looker Conversational Analytics extensions, available now, you can interact with your Looker data and dashboards from the command line, streamlining your workflows and making data more accessible.

These new extensions for the Gemini CLI simplify your ability to ask complex questions of your data, generate insightful reports, and create new dashboards without leaving your terminal, opening up new possibilities for data exploration and analysis from the applications you use every day.

Getting started

Getting started with the new Looker extensions for the Gemini CLI is straightforward. Here’s how you install and configure them:

1. Install the Gemini CLI:

If you haven't already, you'll need to install the Gemini CLI. You can do so via npm:

code_block: <ListValue: [StructValue([('code', 'npm install -g @google/gemini-cli'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff725703a10>)])]>

2. Install the Looker extension:

To install the Looker extension, use the following command:

code_block: <ListValue: [StructValue([('code', 'gemini extensions install \r\nhttps://github.com/gemini-cli-extensions/looker'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff725c995d0>)])]>

3. Install the Looker Conversational Analytics extension:

To install the Looker Conversational Analytics extension, use this command:

code_block: <ListValue: [StructValue([('code', 'gemini extensions install \r\nhttps://github.com/gemini-cli-extensions/looker-conversational-analytics'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7ff725c9bd10>)])]>

4. Configure Your Looker connection:

After installation, you’ll need to configure the extensions to connect to your Looker instance. You’ll need a Looker Client ID and Client Secret, which can be obtained by following the instructions in the Looker API authentication documentation. If you don't have access to the Admin pages of the Looker system, ask your administrator to get the ID and Secret for you.

Then set the following environment variables before starting the Gemini CLI. These variables can also be loaded from a .env file.

LOOKER_BASE_URL: The base URL of your Looker instance (e.g., https://looker.example.com). In some cases, you may need to add :19999 to the URL.
LOOKER_CLIENT_ID: The Looker API client ID
LOOKER_CLIENT_SECRET: The Looker API client secret
LOOKER_VERIFY_SSL: (Optional) Whether to verify SSL certificates. Defaults to true

For looker-conversational-analytics, you also need to provide application default credentials with the correct roles as well as a GCP project with the proper APIs enabled. See https://googleapis.github.io/genai-toolbox/resources/sources/looker/ for all the details. In addition, you need to define these environment variables:

LOOKER_PROJECT: The Google Cloud Project to use
LOOKER_LOCATION: The Google Cloud Location to use, such as us

Dive deeper

The release of these new extensions is a valuable milestone in our efforts to bring the full benefits of Gemini and Looker together for you. To help you get the most out of these new extensions, we've prepared relevant documentation within MCP Toolbox. These resources provide detailed information on all the available tools and functionality.

We welcome your feedback as we continue to innovate. Get started today and unlock the full potential of your data.

Talk with and trust your data using Looker’s Conversational Analytics, now GA

Thu, 13 Nov 2025 17:00:00 +0000

In a world of increasing data volume and demand, businesses are looking to make faster decisions and separate insight from noise. Today, we’re bringing Conversational Analytics to general availability in Looker, delivering natural language queries to everyone in your organization, removing BI bottlenecks. With Conversational Analytics, we’re transforming the way you get answers, cutting through stale dashboards and accelerating data discovery. Our goal: make analytics and AI as easy and scalable as performing a Google search, extending BI to the broader enterprise as you go from prompt to full data exploration in seconds.

Instant AI-powered insights with Conversational Analytics in Looker

We announced the preview of Conversational Analytics at Google Cloud Next in April. Since then, we’ve also extended the ability to leverage Google’s latest Gemini models with your own custom applications through the Conversational Analytics API, showcased productivity improvements that come from the convergence of AI and BI, and empowered today’s modern data analysts and developers to do more, faster.

Now, with Conversational Analytics, getting an answer from your data is as simple as chatting with your most knowledgeable colleague. By tapping into human conversation, Conversational Analytics relieves you from struggling with complex dashboard filters, obscure field names, or the need to write custom SQL.

"At YouTube, we're focused on helping creators succeed and bring their creativity to the world. We've been testing Conversational Analytics in Looker to give our partner managers instant, actionable data that lets them quickly guide creators and optimize creator support." - Thomas Seyller, Senior Director, Technology & Insights, YouTube Business

The general availability of Conversational Analytics combines the reasoning power of Gemini, new capabilities in Google’s agentic frameworks, and the trusted data modeling of the Looker platform. Together, these set the stage for the next chapter in self-service analytics, making reliable data insights accessible to the entire enterprise. Conversational Analytics agents can understand your questions and provide insightful answers to questions about your data.

New at general availability is the ability to analyze data across domains. You can ask questions that integrate insights from up to five distinct Looker Explores (pre-joined views), spanning multiple business areas. Additionally, you can share the agents you build with colleagues, giving them faster access to a single source of truth, speeding consensus, and driving uniform decisions.

You can build and share agents with colleagues to have a consistent data picture.

Built on a trusted, governed foundation

The power of Conversational Analytics isn't just in the conversation it enables; it's in the trust of the underlying data. Conversational Analytics is grounded in Looker’s semantic layer, which ensures that every metric, field, and calculation is centrally defined and consistent, acting as a crucial context engine for AI. As more of your colleagues rapidly use these expanded capabilities, you need to know the results they see and act on are accurate.

For analysts looking to explore data or everyday users receiving insights in the context of their business, Conversational Analytics also improves data self-service, minimizing technical friction that can create bottlenecks and leaves insights locked away.

You can now:

Ask anything, anytime: Get instant answers to simple questions like “Show me our website traffic last month for shoe sales,” leading to deeper questions and greater insights across business areas and domains.
Deepen the discovery: Move beyond the constraints of static dashboards and ask open-ended questions like, “Show me the trend of website traffic over the past six months and filter it by the California region.” The system intelligently generates the appropriate query and visualization instantly.
Extend enterprise BI: Connect your Looker models to your enterprise BI ecosystem, centralize and share agents, and create new dashboards, starting with a prompt. Built on top of Looker Explores, Conversational Analytics’ natural language interface usesLookML for fine tuning and output accuracy.
Pivot quickly: The conversational interface supports multi-turn questions, so you can iterate on your findings. Ask for total sales, then follow up with, "Now show me that as an area chart, broken down by payment method."
Gain full transparency: To build confidence and data literacy, the "How was this calculated?" feature provides a clear, natural language explanation of the underlying query that generated the results, so that you understand the source of your findings.

Empower the BI analyst and business user

Conversational Analytics is democratizing data for business teams, helping them govern the business’s data. At the same time, it’s also enhancing productivity and influence for data analysts and developers.

When business users can self-serve trusted data insights, data analysts see fewer interruptions and “ad-hoc” ticket requests, and can instead focus on high-impact work. Analysts can customize their client teams’ BI experiences by building Conversational Analytics agents that define common questions, filters, and style guidelines, so different teams can act on the same data in different ways.

Get ready to start talking

Conversational Analytics is available now for all Looker platform users. Your admin can enable it in your Looker instance today and you will discover how easy it is to move from simply asking “What?” to confidently determining “What’s next?” For more information, review the product documentation or watch this video tutorial.

Chat with your data from anywhere: Announcing Google’s Conversational Analytics API

Mon, 25 Aug 2025 16:00:00 +0000

August 2026 update: Conversational Analytics in BigQuery and the Conversational Analytics API are now generally available.

Decision-makers, employees, and customers all need answers where they work: in the applications they use every day. In recent years, the rise of AI-powered BI has transformed our relationship with data, enabling us to ask questions in natural language and get answers fast. But even with support for natural language, the insights you receive are often confined to the data in your BI tool. At Google Cloud, our goal is to change this.

At Google Cloud Next 25, we introduced the Conversational Analytics API, which lets developers embed natural-language query functionality in custom applications, internal tools, or workflows, all backed by trusted data access and scalable, reliable data modeling. The API is already powering first-party Google Cloud conversational experiences including Looker and BigQuery data canvas, and is available for Google Cloud developers of all stripes to implement wherever their imagination takes them. Today we release the Conversational Analytics API in public preview. Start building with our documentation.

The Conversational Analytics API lets you build custom data experiences that provide data, chart, and text answers while leveraging Looker's trusted semantic model for accuracy or providing critical business and data context to agents in BigQuery. You can embed this functionality to create intuitive data experiences, enable complex analysis via natural language, and even orchestrate conversational analytics agents as ‘tools’ for an orchestrator agent using Agent Development Kit.

The Google Health Population app is being developed with the Conversational Analytics API

Getting to know the Conversational Analytics API

The Conversational Analytics API allows you to interact with your BigQuery or Looker data through chat, from anywhere. Embed side-panel chat next to your Looker dashboards, invoke agents in chat applications like Slack, customize your company’s web applications, or build multi-agent systems.

This API empowers your team to obtain answers precisely when and where they are needed, directly within their daily workflows. It achieves this by merging Google's advanced AI models and agentic capabilities with Looker's semantic layer and BigQuery's context engineering services. The result is natural language experiences that can be shared across your organization, making data-to-insight interaction seamless in your company's most frequently used applications.

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Building with Google’s Analytics and AI stack comes with significant benefits in accurate question answering:

Best-in-class AI for data analytics
An agentic architecture that enables the system to perceive its environment and take actions
Access to Looker’s powerful semantic layer for trustworthy answers
High-performance agent tools, including software functions, charts and APIs, supported by dedicated engineering teams
Python code interpreter for advanced analysis
Tune your agent’s knowledge with structured context and prompts

Flexibility to build the agentic applications that best suit your data needs:

Create, update, and share agents that let users chat with BigQuery or Looker data
Lower maintenance burden via stateful APIs for agent and conversation management
Full control of your user experience via our stateless chat API
Build multi-agent architectures by wrapping APIs with ADK and MCP
Help agents understand your business and data with AI-Assisted context engineering
Version control your agents, updating prompts without affecting production use

And the enterprise controls and security you expect from Google Cloud Platform:

Govern the use of agents using role based access controls
Trust your data is secure with data with row and column level access controls by default
Guard against expensive queries with built-in query limitations

When pairing Conversational Analytics API with Looker, Looker’s semantic layer reduces data errors in gen AI natural language queries by as much as two thirds, so that queries are sourced in truth.

Looker’s semantic layer ensures your conversational analytics are grounded in data truth.

An agentic architecture powered by Google AI

The Conversational Analytics API uses purpose-built models for querying and analyzing data to provide accurate answers, while its flexible agentic architecture lets you configure which capabilities the agent leverages to best provide users with answers to their questions.

Conversational Analytics leverages an agentic architecture to empower agent creators with the right tools.

As a developer, you can compose AI-powered agents with the following tools:

Text-to-SQL, trusted by customers using Gemini in BigQuery
Context retrieval, informed by personalized and organization usage
Looker's NL-to-Looker Query Engine, to leverage the analyst-curated semantic layer
Code Interpreter, for advanced analytics like forecasting and root-cause analysis
Charting, to create stunning visualizations and bring data to life
Insights, to explain answers in plain language

These generative AI tools are built upon Google’s latest Gemini models and fine-tuned for specific data analysis tasks to deliver high levels of accuracy. There’s also the Code Interpreter for Conversational Analytics, which provides computations ranging from cohort analysis to period-over-period calculations. Currently in preview, Code Interpreter turns you into a data scientist without having to learn advanced coding or statistical methods. Sign up for early access here.

Context retrieval and generation

A good data analyst isn’t just smart, but also deeply knowledgeable about your business and your data. To provide the same kind of value, a “chat with your data” experience should be just as knowledgeable. That’s why the Conversational Analytics API prioritizes gathering context about your data and queries.

Thanks to retrieval augmented generation (RAG), our Conversational Analytics agents know you and your data well enough to know that when you’re asking for sales in “New York” or “NYC,” you mean “New York City.” The API understands your question’s meaning to match it to the most relevant fields to query, and learns from your organization, recognizing that, for example, “revenue_final_calc” may be queried more frequently than “revenue_intermediate” in your BigQuery project, and adjusts accordingly. Finally, the API learns from your past interactions; it will remember that you queried about “customer lifetime value” in BigQuery Studio on Tuesday when you ask about it again on Friday.

Not all datasets have the context an agent needs to do its work. Column descriptions, business glossaries, and question-query pairs can all improve an agent’s accuracy, but they can be hard to create manually— especially if you have 1,000 tables in your business, each with 500 fields. To speed up the process of teaching your agent, we are including AI-assisted context, using Gemini to suggest metadata that might be useful for your agent to know, while letting you approve or reject changes.

Low maintenance burden

The Conversational Analytics API gives you access to the latest data agent tools from Google Cloud, so you can focus on building your business, not building more agents. You benefit from Google’s continued advancements in generative AI for coding and data analysis.

When you create an agent, we protect your data with Google’s security, best practices, and role-based access controls. Once you share your Looker or BigQuery agent, it can be used across Google Cloud products, such as Agent Development Kit, and in your own applications.

The Conversational Analytics API lets you interact with your data anywhere.

API powered chats from anywhere

With agents consumable via API, you can surface insights anywhere decision makers need them—whether it’s when speaking with a customer over a support ticket, via a tablet when you’re in the field, or in your messaging apps.

The Conversational Analytics API is designed to bring benefits to all users, whether they be business users, data analysts building agents, or software developers. With Conversational Agents, when a user asks a question, answers are delivered rapidly alongside the agent’s thinking process, to ensure the right approach to gaining insights is used. Individual updates allow your developers to control what a user sees — like answers and charts — and what you want to log for later auditing by analysts — like SQL and Python code.

To get started, you can use our documentation and API references for REST APIs and SDKs, as well as code examples in example Colab notebooks, streamlit application on GitHub, and typescript reference application.

Business Intelligence

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What’s new with Google Data Cloud - 2025

Unifying enterprise context and semantics: LookML in Knowledge Catalog is now GA

Why a context graph needs deterministic semantics

What's new: LookML in Knowledge Catalog

Three patterns for data agents

Scaling agents for BI and the enterprise

Getting started

Trusted Data Apps for the Agentic Data Cloud, powered by Looker

What is a data app?

Expanding your semantic layer into an application platform

Built for enterprise scale

From prototype to production in minutes

Looker’s semantic layer governs Gemini Enterprise data for user trust

Bringing a semantic foundation to structured and unstructured data

Minimize AI hallucinations

Robust governance and secure access management

Technical capabilities and enterprise readiness

Trusted data in Gemini Enterprise

Automate data monitoring and root-cause analysis with Looker Agentic Workflows

Deliver root-cause analysis in Slack and email

Investigate deeper with central oversight

Get started with Looker Agentic Workflows

Bringing Conversational Analytics to your entire data ecosystem

Query across multi-cloud and database workloads

Proactive insights with Agentic Workflows

Flexible integration with APIs, SDKs, and MCP

Get started with Conversational Analytics

Google named a Leader in 2026 Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms for third year in a row

The universal semantic layer agents

Powered by Gemini’s reasoning capabilities

Leaping into the Agentic BI era with Looker and Gemini

Introducing new Explores and Merge Queries in Looker

A team of AI assistants

AI-assisted Quick Start

Insight Assistant

Expression Assistant

AI-generated Explore summary

An intuitive, modernized UI

What’s new in data agents: Supercharging your AI workflows

1. Conversational Analytics

2. New data agents

3. Tools for data agents

Transform dashboards into interactive data experiences with Looker agents

Interactive agent-led investigations

Tailor the agent to your business

Inherited trust and transparency

Enable dashboard agents today

What’s new in the Agentic Data Cloud: Powering the System of Action

Enabling agents with a universal context engine

Agentic-first practitioner experiences

A cross-cloud foundation built for agentic scale

Automating the future

Build your success on a System of Action

Welcome to the agentic BI era with Looker

Conversational BI and agents: From conversations to actions

Reimagining self-service: AI power, UI control

A trusted and open platform

Data Studio returns as new home for Data Cloud assets