Cloud Migration

Migrating to Google Cloud’s Application Load Balancer: A practical guide

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

Migrating your existing application load balancer infrastructure from an on-premises hardware solution to Cloud Load Balancing offers substantial advantages in scalability, cost-efficiency, and tight integration within the Google Cloud ecosystem. Yet, a fundamental question often arises: "What about our current load balancer configurations?"

Existing on-premises load balancer configurations often contain years of business-critical logic for traffic manipulation. The good news is that not only can you fully migrate existing functionalities, but this migration also presents a significant opportunity to modernize and simplify your traffic management.

This guide outlines a practical approach for migrating your existing load balancer to Google Cloud’s Application Load Balancer. It addresses common functionalities, leveraging both its declarative configurations and the innovative, event-driven Service Extensions edge compute capability.

A simple, phased approach to migration

Transitioning from an imperative, script-based system to a cloud-native, declarative-first model requires a structured plan. We recommend a straightforward, four-phase approach.

Phase 1: Discovery and mapping

Before commencing any migration, you must understand what you have. Analyze and categorize your current load balancer configurations. What is each rule's intent? Is it performing a simple HTTP-to-HTTPS redirect? Is it engaged in HTTP header manipulation (addition or removal)? Or is it handling complex, custom authentication logic?

Most configurations typically fall into two primary categories:

Common patterns: Logic that is common to most web applications, such as redirects, URL rewrites, basic header manipulation, and IP-based access control lists (ACLs).
Bespoke business logic: Complex logic unique to your application, like custom proprietary token authentication, advanced header extraction / replacement, dynamic backend selection based on HTTP attributes, or HTTP response body manipulation.

Phase 2: Choose your Google Cloud equivalent

Once your rules are categorized, the next step involves mapping them to the appropriate Google Cloud feature. This is not a one-to-one replacement; it's a strategic choice.

Option 1: the declarative path (for ~80% of rules)
For the majority of common patterns, leveraging the Application Load Balancer's built-in declarative features is usually the best approach. Instead of a script, you define the desired state in a configuration file. This is simpler to manage, version-control, and scale.

Common patterns to declarative feature mapping:

Redirects/rewrites -> Application Load Balancer URL maps
ACLs/throttling -> Google Cloud Armor security policies
Session persistence -> backend service configuration

Option 2: The programmatic path (for complex, bespoke rules)
When dealing with complex, bespoke business logic, you have a programmatic equivalent: Service Extensions, a powerful edge compute capability that allows you to inject custom code (written in Rust, C++ or Go) directly into the load balancer's data path. This approach gives you flexibility in a modern, managed, and high-performance framework.

This flowchart helps you decide the appropriate Google Cloud feature for each configuration

Phase 3: Test and validate

Once you’ve chosen the appropriate path for your configurations, you are ready to deploy your new Application Load Balancer configuration in a staging environment that mirrors your production setup. Thoroughly test all application functionality, paying close attention to the migrated logic. Use a combination of automated testing and manual QA to validate the redirects, security policies, and that the custom Service Extensions logic are behaving as expected.

Phase 4: Phased cutover (canary deployment)

Don't flip a single switch for all your traffic; instead, implement a phased migration strategy. Start the transitioning process by routing a small percentage of production traffic (e.g., 5-10%) to your new Google Cloud load balancer. During this initial period, be sure to monitor key metrics like latency, error rates, and application performance. As you gain confidence, you can progressively increase the percentage of traffic routed to the Application Load Balancer. Always have a clear rollback plan to revert back to the legacy infrastructure in the event you encounter critical issues.

Best practices for a smooth migration

Drawing from our practical experience, we have compiled the following recommendations to assist you in planning your load balancer migrations.

Analyze first, migrate second: A thorough analysis of your existing configurations is the most critical step. Don't "lift and shift" logic that is no longer needed.
Prefer declarative: Always default to Google Cloud's managed, declarative features (URL Maps, Cloud Armor) first. They are simpler, more scalable, and require less maintenance.
Use Service Extensions strategically: Reserve Service Extensions for the complex, bespoke business logic that declarative features cannot handle.
Monitor everything: Continuously monitor both your existing load balancers and Google Cloud load balancers during the migration. Watch key metrics like traffic volume, latency, and error rates to detect and address issues instantly.
Train your team: Ensure your team is trained on Cloud Load Balancing concepts. This will empower them to effectively operate and maintain the new infrastructure.

Migrating from the existing on-premises load balancer infrastructure is more than just a technical task, it's an opportunity to modernize your application delivery. By thoughtfully mapping your current load balancing configurations and capabilities to either declarative Application Load Balancer features or programmatic Service Extensions, you can build a more scalable, resilient, and cost-effective infrastructure destined for future demands.

To get started, review the Application Load Balancer and Service Extensions features and advanced capabilities to come up with the right design for your application. For more guidance and complex use cases, contact your Google Cloud team.

Accelerate migrations with new incentives from the Rapid Migration and Modernization Program (RaMP)

Wed, 21 Jan 2026 16:00:00 +0000

To lead in 2026, you need to be AI-ready, lean, and optimized across every workload — and for many organizations, that means migrating and modernizing applications like SAP, Oracle, NetApp, and VMware to the cloud. At Google Cloud, we’ve helped thousands of customers with successful migrations through the Rapid Migration and Modernization Program (RaMP), and today, we’re introducing the new RaMP, so that the more you migrate, the more you save. Highlights of the new program include:

Google Cloud Service Credits: Earn credits based on your incremental usage of eligible workloads on Google Cloud.
Partner and Google Cloud Professional Services funds: We will fund partners and Google Cloud Professional Services to help you assess your needs, build your business case and implement your migration and modernization roadmap.
Earn more with advanced workloads: Earn additional credits and incentives for advanced workloads (like SAP, Oracle, VMware, Data Analytics, etc.) to offset higher technical costs.

From technical debt to AI readiness

You deserve a migration and modernization path that leads to less cost and complexity, not more. Moving to Google Cloud provides the infrastructure foundation you need to replace your technical debt with flexibility, so you can reduce cost and complexity.

But migrating and modernizing your infrastructure is about more than that — it’s about data accessibility, optimization, and innovation. For example, when you migrate a legacy SAP environment or a massive Oracle database to Google Cloud, you aren't just changing where the data sits; you are making that data accessible to Vertex AI and our Gemini models.

RaMP can accelerate your migration and replace technical debt with a scalable, secure foundation that can support existing enterprise workloads and the next generation of AI applications — whatever they may be.

Building for the future

Migrating and modernizing with RaMP gives you immediate access to world-class infrastructure, data, and AI solutions, giving you the foundation you need to succeed in 2026 and beyond. To get started, visit our RaMP page to learn more and start your assessment. Get ready to rapidly enter the AI era. Welcome to the fast lane.

Accelerating cloud migrations to Google Cloud with Searce to drive profitable growth

Thu, 25 Sep 2025 16:00:00 +0000

As companies transition past legacy infrastructure and set themselves up for growth in AI, multi-cloud, and platform engineering requirements, many are looking to Google Cloud for its reliability, performance, and cost benefits.

To achieve successful migrations, especially at the enterprise level, organizations need a seasoned partner with a deep understanding of platform modernization, automation, and business outcomes. Searce is one of the Google Cloud partners helping several organizations migrate to Google Cloud and achieve striking business value.

Searce has led over 1,000 migrations

Searce is an engineering-led, AI-powered consultancy and Google Cloud Premier partner that has delivered over 1,000 successful migrations to date. Their focus: helping businesses across industries scale faster, modernize legacy platforms, and unlock new efficiencies with Google Cloud.

Through hands-on experience, Searce has identified three consistent, measurable benefits that enterprises gain when migrating to Google Cloud:

Improved reliability and integration with cloud-native services (e.g., 25% improved reliability)
Increased productivity with reduced costs (e.g., 50% reduction in TCO)
Scalability and performance (e.g., Petabytes of data migrated with 75% reduced downtime and 30% performance improvement)

Together, Google Cloud’s Global VPC, leadership in AI/ML, and powerful managed container services like Google Kubernetes Engine and GKE Autopilot enable these transformations. Searce combines these capabilities with its engineering-led, solution-oriented approach to deliver impact fast — especially for organizations modernizing legacy platforms or scaling AI workloads. Let’s take a look at a few examples of migrations to Google Cloud that Searce has performed.

Boosting reliability in healthcare with GKE Gateway Controller

A major digital healthcare provider that treats more than 50 million patients per year required near 100% platform availability. Their legacy Kubernetes infrastructure on a different cloud provider would regularly have unplanned outages during microservices upgrades, which postponed care, interrupted pharmacies, and eroded patient trust.

Searce upgraded their platform to GKE, replacing legacy ingress resources with GKE Gateway Controllers to break apart routing and facilitate safer, quicker rollbacks. The outcome? Searce said the healthcare company saw:

25% increase in platform reliability
30% reduction in infrastructure expense
Streamlined log aggregation with Cloud Logging and single resource management with Global VPC architecture

With GKE, they now roll out updates securely, minimizing downtime and speeding up development without sacrificing availability.

50% lower TCO for a global fintech leader with GKE Autopilot

With operations in over 20 countries, an international fintech company used several Kubernetes clusters to achieve performance at scale, but managing this sort of global deployment had a price. Operational overhead was sucking engineering time and momentum out of innovation.

By moving them to GKE Autopilot, Searce assisted in unifying service management and transferring operational complexity to Google Cloud. With global HTTP load balancing, self-managing Infrastructure as Code (IaC) modules, and team-specific resource management, Searce said its customer’s business experienced:

50% reduction in total cost of ownership
40% improvement in engineering productivity
Reduced time-to-market for customer-facing features

This transformation enabled teams to concentrate on what is most important: creating value for users, not dealing with infrastructure.

Scaling performance for a global telecom giant

A leading global telecommunication provider with petabytes of data and over 150 years of history needed to be able to scale without compromises. Their traditional systems constrained performance and required unacceptably high amounts of downtime for maintenance.

They joined forces with Searce to plan a smooth transition to Google Cloud, optimizing storage and compute for AI workloads, and combining high-throughput data pipelines.

Searce and the customer were able to realize:

30% boost in system performance
75% decrease in downtime during migration
Increased customer innovation and data-driven services for a future-proof foundation

How Searce delivers migration success

With over 1000 successful migrations under its belt, Searce brings deep engineering expertise and a proven framework to help enterprises move to Google Cloud with speed, confidence, and long-term value.

At the core is Searce’s ‘evlos’ (Solve differently) approach to simplify, optimize, and future-proof enterprise workloads across infrastructure, data, applications, and AI.

A proven five-step migration framework

Searce’s structured approach ensures a smooth transition from discovery through Day-2 operations:

Discovery and assessment – Identify workloads and dependencies
Planning – Define migration, DR, and rollback strategies
Design, migrate and test – Wave-based automation with minimal downtime
Execution – Cutover and system stabilization
HyperCare – 24x7 post-migration support via Searce’s in-house SOC team

To help enterprises through their journey to Google Cloud and further streamline the customer journey, Searce offers three key accelerators:

Automated Cost Savings Assessment – 24-hour TCO report
Automated Technical Assessment – Secure, software-driven inventory
Automated Migration to Google Cloud – Script-based automation for faster cutovers

Ready to accelerate your migration journey?

Enterprises worldwide are choosing Google Cloud to build scalable, reliable, and AI-ready platforms. With proven migration experience, domain expertise, and a unique approach to transformation, Searce is the trusted partner to get you there faster. Backed by Google Cloud’s capabilities, Searce turns migration into a strategic enabler for innovation, not just a technical shift.

Explore what’s possible when you migrate with confidence. Access the whitepaper using this link to get more details.

Simplify your data platform migration with AI-powered BigQuery Migration Services

Thu, 10 Apr 2025 16:00:00 +0000

Migrating data workloads to BigQuery, our unified Data to AI platform, just got significantly easier. You no longer have to choose between unlocking value from your data assets by migrating to a modern data platform, or mitigating risk by staying put. You can achieve both with BigQuery Migration Services, a collection of free-to-use, cloud-native services that enable large-scale transformations for data warehouses and data lakes by breaking down migrations into templated, iterative and manageable steps. They move data, code, and business logic from on-premises and cloud platforms to BigQuery, utilizing a “next-best action” approach that minimizes time-to-migrate and maximizes ROI for your business transformation.

At Google Cloud Next 25, we announced several new innovations in BigQuery Migration Services, including coverage for data science and expanding support for data engineering and data analytics workloads. New capabilities span across four stages of a data platform migration: 1) automated assessment and planning, 2) automatic code translation, 3) data migration, and 4) validation.

BigQuery Migration Services

Let’s look at the new innovations in BigQuery Migration Services.

1. Automated discovery and assessment with estimated total cost of ownership

Your data platform migration journey begins with automated discovery and assessment of the source environment. BigQuery Migration Services’ automated assessments provide details of the existing environment, create an insights-filled view of the workloads’ projected landed state on BigQuery (including performance and estimated total cost of ownership), and guide you on how to get to BigQuery (migration planning). You can run an assessment with the push of a button on the Google Cloud console, which delivers a detailed Looker-studio report and BigQuery datasets as output. Assessments are available for Teradata, Snowflake, and Redshift, and today, we also announced that assessments for Oracle/Exadata and Cloudera/Hive are available immediately, and that a Databricks assessment is coming soon.

To help with a structured and successful migration, we also announced a source lineage service in preview. This service automatically identifies and groups dependencies between workloads, creating an explicit ordering in which to move them, helping to minimize risk and disruption, and improving time-to-value.

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2. Automated code translations

Some of our heaviest investments in BigQuery Migration Services over the past years have been in our code translation services, which migrate code from 15+ sources. Today, we announced advancements in Gemini-enhanced code translation, which was previously only available in interactive mode, letting you translate code, like you would with, say, Google Translate.

Now, Gemini-enhanced code translations are also available in batch and API modes, helping you migrate at scale. Coupled with a new unified Translation API that backs all three modes, you can first translate bulk code using batch or API modes, and then fine-tune and debug it using interactive mode.

Gemini-enhanced translations

Now, you can also preprocess your code with Gemini, so you can migrate code that’s not just SQL, but also other kinds of code, e.g., an ETL job with SQL embedded inside XML. This means you don’t need to submit perfectly clean SQL, and can translate code from sources that don’t have full compiler coverage yet.

Finally, there’s an enhanced user-experience in the console to guide you at each step of the translation process, suggesting the next-best action to get you to the finish line.

Enhanced User Experience

These advancements dramatically reduce code conversion times while continuing to deliver over 95% accuracy, helping you tackle large migration jobs with greater efficiency.

3. Data, metadata and permissions migration

Historically, BigQuery Migration Services have supported large-scale data migrations from Teradata and Amazon Redshift. Today, BigQuery Migration Services support incremental updates from Teradata, batch and incremental file and permission migrations from Cloudera, and batch and incremental data migration from Snowflake, all in preview. All migrated data is automatically validated as part of the migration process.

4.Intelligent end-to-end validation

Each step of the migration process will soon include an intelligent validation mechanism that can incorporate schema and data-type updates, vs. static data checksum comparisons that exist today. You can combine validation with source lineage, making it easy to quickly identify discrepancies between source and target environments. This comprehensive code, data, and dependency validation helps ensure your business applications stay intact as you incrementally move them.

Together, these investments in each of the four stages of a data platform migration help automate your journey while containing risk, providing deterministic outcomes, and faster ROI.

Customer successes

Customers trust BigQuery Migration Services for migrating their mission-critical workloads. BigQuery Migration Services usage has grown 3x year over year, with thousands of customers using the services to migrate workloads to BigQuery.

“By migrating from Databricks to BigQuery and combining our own models with the models provided by Google Cloud, we’ve improved the performance and efficiency of our machine learning processes and better positioned ourselves for ongoing growth.” - Hamdi Amroun, Head of AI, Yassir

"BigQuery has unlocked unprecedented scalability and flexibility for VMO2, improving data platform availability and uptime, which ultimately enhances customer experience. By moving all key functions to Google Cloud, VMO2 has reduced its TCO for equivalent on-premises platforms by approximately 30%." - Vinay Pai, Head of Data Architecture, Virgin Media O2

Take the next steps

Ready to start migrating your data platform to BigQuery? We’re ready to help!

Learn more about BigQuery Migration Services and try it for free.
Connect with Google Cloud Consulting and select migration partners standardized on BigQuery Migration Services to achieve predictable and cost-effective outcomes.
Sign up today for the BigQuery migration incentives program for additional benefits such as Google Cloud credits, implementation services and cloud egress credits.

Cloud migrations made easy: a guide to migrating from AWS to Google Cloud

Mon, 27 Jan 2025 17:00:00 +0000

More and more customers deploy their workloads on Google Cloud. But what if your workloads are sitting in another cloud? Planning, designing, and implementing a migration of your workloads, data, and processes is not an easy task. It gets even harder if you have to meet requirements that have an impact on the migration, such as avoiding downtime (also known as a zero-downtime migration). Moreover, some migrations require a certain amount of refactoring, for example, adapting your workloads to a new environment. This opens up a series of challenges, especially if you’re dealing with third-party or legacy software. You might also need to adapt your deployment and operational processes to work with your new environment.

And what if you don’t want to migrate all your workloads? Even if you’re not moving everything to Google Cloud, adopting a multicloud approach is still a migration. Many organizations choose to keep some workloads in their current cloud provider while moving others to Google Cloud.

Although managing workloads across multiple clouds has its own challenges, particularly when it comes to workload distribution and inter-cloud connectivity, a well-executed multicloud strategy lets you maintain flexibility, avoid vendor lock-in, and improve system resilience.

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To help you in your migration journey, we published a series of reference guides about migrating from Amazon Web Services (AWS) to Google Cloud. This series aims to help you design, plan, and implement a migration process from AWS to Google Cloud. It can also help decision makers who are evaluating migration opportunities and want to explore what it looks like to migrate. For example, the series includes guides that cover migration journeys, such as:

From AWS compute services to Google Cloud

From AWS data storage services to Google Cloud

Migrate from Amazon S3 to Cloud Storage

From AWS database services to Google Cloud

From AWS data analytics services to Google Cloud

Migrate from Amazon RedShift to BigQuery

From AWS integration services to Google Cloud

Migrate from AWS Step Functions to Workflows

These guides follow the phases of the Google Cloud migration framework (assess, plan, migrate, optimize) in the context of specific AWS to Google Cloud migration use cases.

This approach helps to avoid big-bang, risky migrations, when working on each migration plan task. For details about completing each task of this migration plan, see the AWS to Google Cloud migration guides.

What’s next

Learn how to get started with your migration from AWS to Google Cloud today!
In case you need any help, Google Cloud offers several resources to help you migrate your workloads, data, and processes at scale.
Need a more visual guide? Download our new migration guide & checklist.

Ready to get started?

Ready to learn more? Learn more about migrating to Google Cloud and discover how Google Cloud Consulting can help you learn, build, operate and succeed.

^{Acknowledgements: Alex Cârciu, Cloud Solutions Architect, Xiang Shen, Cloud Solutions Architect}

Migrate Oracle-based applications to Google Cloud and simplify operations

Thu, 23 Jan 2025 17:00:00 +0000

Last year, Google Cloud and Oracle forged a strategic partnership to accelerate cloud transformation for businesses, allowing them to integrate Oracle's robust database capabilities within Google Cloud's environment.

This partnership applies to Oracle databases, as well as the applications that run alongside them. That means migrating existing Oracle database applications to Google Cloud, where customers enjoy the benefits of our performance, stability and security. Oracle also offers a variety of enterprise and middleware applications that aren’t databases per se that you may want to migrate. And of course, you can build and deploy new cloud-native applications using Oracle databases and services on Google Cloud's services and infrastructure.

This blog post explores migrating Oracle applications and databases to Google Cloud. It details various migration paths, from fully managed services to customized options, with an emphasis on how containerization and GraalVM can help optimize performance and scalability.

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Oracle databases in Google Cloud

Google Cloud offers several migration paths for your Oracle workloads, from fully managed to highly customized, offering flexibility depending on your needs.

High performance:

Exadata Cloud Service in Google Cloud: Run Oracle's high-performance hardware, Exadata, directly within Google's data centers. This is ideal for demanding workloads that need the maximum available speed and power.

Fully managed:

Autonomous Database on Google Cloud: Google Cloud handles everything, making it super easy to run Oracle. It automates all the essential management tasks — provisioning, patching, scaling, backup, and recovery — so you can focus on what matters most to your business.

As highly managed offerings, both of the above migration paths require very few (if any) changes to your Oracle Database instances, and the physical migration itself can be performed by our support team.

Flexible options:

When you choose one of these flexible options, however, there are several things to think about to get the best possible performance from your new cloud-based environment.

Compute Engine: Run Oracle on virtual machines, giving you control and flexibility. This is good for "lift and shift" migrations of existing systems.
Google Kubernetes Engine (GKE): Using OraOperator, Oracle’s Database Operator for Kubernetes, you can deploy Oracle pluggable databases; you can read more about the use cases for this below.

Containerize Oracle-based applications

This partnership is designed to provide businesses with the flexibility they need to deploy and manage Oracle applications on Google Cloud, accelerating migration, modernization and innovation. A key part of that is support for containerization, which gives development teams high levels of agility and scalability. Whether your Oracle-based application is built with Java, Python, .NET, or runs on WebLogic, containers provide a consistent and isolated environment, abstracting away the underlying complexities of programming languages and frameworks.

Google Cloud offers a wide range of container-based runtimes for you to choose from.

Google Kubernetes Engine (GKE) is a powerful and versatile container orchestration platform that simplifies the deployment and management of diverse workloads on Google Cloud. Designed for businesses running complex setups and microservices architectures, GKE leverages Google's global network and high-performance VMs to provide an optimized infrastructure for running containers smoothly and reliably. It automatically adjusts resources to meet real-time demand, optimizing both cost and performance through autoscaling and node auto-provisioning. Moreover, GKE's broad support for container runtimes and advanced networking capabilities helps it efficiently handle everything from basic web applications to demanding machine learning workloads, written in any language.

Then there’s Cloud Run, a fully managed serverless platform for deploying containerized applications. Designed for applications that need to scale automatically based on demand, you simply provide your code or container image and Cloud Run takes care of the rest. It automatically scales resources up or down in response to traffic, meaning you only pay when your application is actively processing requests. By eliminating server management tasks like provisioning and scaling, Cloud Run frees you to concentrate on building and deploying applications, making it a good fit for microservices, APIs, web apps, and event-driven functions. Further, Cloud Run's automatic base image updates helps your containers benefit from the latest security patches and operating system upgrades, minimizing vulnerabilities and significantly reducing maintenance overhead.

Best of all, containerizing your Oracle based applications with open standards means you don't have to choose your path upfront. Don't want to manage Kubernetes? Start with Cloud Run! Need more flexibility later on? Easily move to GKE. Because both services leverage standard OCI containers and are supported by CNCF, you have full interoperability and freedom of deployment choice for your containerized apps, now and in the future.

Running a containerized Oracle Database

Running your Oracle workload on Exadata within Google Cloud is the gold standard; however if that is not possible then you may want to explore running Oracle on GKE:

Running Oracle Database on GKE is suitable for non-RAC and non-Exadata workloads, or when Exadata is not supported in a given region.
This is a good solution for businesses that use a DBaaS strategy and have many Oracle Databases that can be lifecycled (deployed, updated, removed) using OraOperator.
Designed for integration within modern DevOps workflows and continuous deployment strategies, this approach is useful if you need to spin up or tear down databases quickly.

In this context, the afore-mentioned OraOperator provides multi-tenancy support. This is a valuable feature for running Oracle databases on GKE, allowing multiple isolated databases to run on the same GKE cluster, helping to improve resource utilization and reduce costs.

Turbocharging your Java applications

Many Oracle applications are written in Java. Oracle GraalVM creates native Java applications. This tool uses Ahead-of-Time (AOT) compilation, converting Java bytecode directly into native executables. By removing the reliance on a Java Virtual Machine (JVM) during execution, these applications start much faster and consume less resources (CPU and memory). GraalVM essentially allows Java to behave like languages such as C or C++, producing standalone executables that are perfect for resource-constrained environments like microservices and serverless functions in cloud-native deployments.

If you want to run your Oracle app on GKE, we highly recommend using a Java native image, which allows for highly optimized resource utilization compared with running a Java application from within a VM. That’s because native images have a smaller footprint, allowing you to run more pods per worker node, maximizing your GKE cluster resources and potentially reducing costs. Reduced pod startup latency also contributes to faster rollout and recovery times for deployments, improving the agility of your application.

And finally, using a Java native image enhances the security posture of your containers within the GKE cluster through the use of features such as:

image signing and verification with binary authorization
advanced vulnerability insights, which provides scanning and vulnerability detection in Java, Go, Javascript, and Python language packages of your running containers
vulnerability scanning built into Artifact Registry, using a sandboxed environment to provide a secure and isolated environment for your Java native images
minimal base images that only include necessary dependencies in your Java native image, reducing the potential attack surface and minimizing the risk of vulnerabilities

Meanwhile, running your Java native application on Cloud Run provides near-instantaneous startup times, minimizing latency for initial requests and improving overall responsiveness. The Cloud Run startup CPU boost feature provides additional CPU during instance startup time and for 10 seconds after the instance has started.

In addition, Cloud Run is well-suited for event-driven applications, where functions are triggered by events. This is a common use case for native Java images, which, with their lack of a Java Runtime Environment, can respond to events quickly.

Finally, many organizations want to transition to Java 21. This adoption is largely motivated by the advantages of its LTS (long term support) designation which guarantees ongoing updates, security patches, increased runtime performance, new features and technical support for an extended period. Migrating older Oracle applications to GKE or Cloud Run is a good opportunity to modernize them to Java 21 at the same time.

Support from our partners

Moving your Oracle databases and applications to Google Cloud is a big task but you don't have to go it alone. Google Cloud has partnered with experienced Oracle experts to help make your migration smooth and successful. These partners bring deep knowledge of Oracle technologies and proven methodologies to guide you through the process. Reach out to our ecosystem of partners who are available to help accelerate the success of your Oracle workloads on Google Cloud.

Discover and assess your database workloads migration to Google Cloud using Migration Center

Wed, 22 Jan 2025 17:00:00 +0000

Organizations continue to adopt the public cloud to deliver better business and IT outcomes. However, migration — and modernization — is a complex, multifaceted challenge. It involves understanding the current state of infrastructure and applications to determine what the future state should look like and how to get there. And all this occurs across multiple stakeholders using myriad tools, processes, and implementation partners. But wait, don’t let that scare you off – we have good news, too!

Google Cloud Migration Center, a unified platform to help accelerate migration from current on-premises or cloud environments to Google Cloud, is designed to streamline your cloud journey with intelligent, data-driven insights and actionable recommendations. This will help you make critical decisions on the optimal migration and modernization pathways for your organization.

Migration Center lets you discover and assess both servers and databases, which are critical components of your IT infrastructure. In this blog we will focus on databases discovery and assessment journey within Migration Center.

The databases discovery and assessment journey consists of three main phases:

Discovery: Collect database configuration (metadata only) using open-source collection scripts. Supported databases are Microsoft SQL Server, MySQL and PostgreSQL.
Technical fit assessment: Evaluate technical fit of source databases to Cloud SQL and/or AlloyDB along with a detailed technical report.
Cost estimate: Assess overall costs of running source databases in Cloud SQL or AlloyDB in different migration scenarios (e.g., in various regions, using multiple vs. single zones, etc.) using various rightsizing algorithms. You can combine cost estimates of your server and database migration in a single report to create a holistic estimate of your infrastructure migration to Google Cloud.

Let’s look at each of these in greater depth.

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Discovery and collection of source database details

Database configuration and metrics collection Collecting Microsoft SQL Server, MySQL and PostgreSQL source database configurations is executed using open-source collection scripts.

You can run the script in your source system either in the database server directly or from a remote server that has access to the database. Here is an example for running the script from a bastion/jump server:

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Upon completion the script automatically creates an archive of the extracted configuration and metrics (one ZIP file per database instance) that you can upload into Migration Center.

Note: If you are running the script from a bastion/Jump server and not directly the database server, then pass the Parameter “--vmUserName” to collect the host’s specification details.

Uploading discovery artifacts to Migration Center

Once you’ve generated the output files, you can upload them to Migration Center to perform the product assessment by following these steps:

Open Google Cloud console > MIgration Center home page. Go to Discovery > Data Import > Add Files > Upload Files
In the next step, choose the file format as “Database Import File” and then upload all your DMA output zip files and click on Upload Files.
You can now review the uploaded database deployments in Migration Center Assets section.

Technical fit assessment

You can find all the discovered servers and databases in the Assets section of Migration Center. The list of database deployments surfaces high-level details of discovered databases along with a high-level fit score for Cloud SQL and AlloyDB.

To review a detailed configuration, click on the source databases you want to assess, and the available data: source logical database configurations, hosting server configurations, performance data, feasible cloud products, the fit score, and target instance sizing.

You can also review the technical fit report by clicking on the fit score (e.g., fit with effort), which lists unsupported features/parameters/flags that were identified in the source databases and that will need to be mitigated when migrating to Cloud SQL or AlloyDB.

Cost estimates

Assessing the cost of running servers and databases in the cloud in Migration Center is a three-step process:

Group together the servers and/or databases (in one or multiple groups) to be assessed.
Create one or more migration preferences that will serve as migration scenarios to assess.
Create a TCO report by assigning groups to up to four preferences.

1. Create group

To create the pricing report, you need to create a group using the assets you want to evaluate for cost estimates. Go to Discovery -> Groups -> Create Group.

Select the relevant servers and/or databases that you would like to assess as a group. We recommend creating groups that categorize your source environment (e.g., applications, environments, etc.).

2. Create migration preferences

Migration preferences are basically a set of parameters that you can define to represent a migration scenario. You can create multiple migration preferences and use them to generate “what if” scenarios in TCO reports to compare costs for the servers and databases in the cloud. For example, you can create migration preferences for different editions of Cloud SQL with options for high availability and backups enabled.

To create a migration preference, go to Migration Center > Discovery > Migration Preferences > Create MIgration Preference.

3. Create a TCO report for your server and database workloads

Go to Reports -> Create Reports -> TCO and detailed pricing report to start creating the report.
Select your Group/s of assets to assess.
Assign up to four migration preferences to each group as an input.

In the TCO report you can review source environment details, cost estimates for servers and databases, and export the report in Google Slides or CSV formats. Below is an example of the databases cost estimates:

With Migration Center, our goal is to put the information you need when doing a migration and modernization project at your fingertips, so you can reduce the time, energy, and cost of these projects. As you navigate your journey to the cloud, we’re here to help with Migration Center. Start using Migration Center today, or to learn more, check out the web pages for Migration Center and Rapid Discovery and Assessment Program (RAMP).

(Re)Introducing IBM Power for Google Cloud

Mon, 02 Dec 2024 17:00:00 +0000

Back in January of 2020, we announced the availability of IBM Power Systems for Google Cloud. But while the pandemic accelerated cloud computing adoption, many large enterprises still faced challenges with critical workloads such as those often found on the Enterprise IBM Power platform.

At the beginning of 2022, we partnered with Converge Technology Solutions, a company with deep expertise in this market, to expand our support for customers with IBM Power Workloads. Converge was already an important partner and they have upgraded the service by enhancing network connectivity to Google Cloud along with bringing full support to the IBM i operating system.

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Today, Converge Enterprise Cloud with IBM Power for Google Cloud, or simply IP4G, supports all three major environments for Power: AIX, IBM i and Linux. In addition, it’s now available in four new regions in production — two in Canada and two in EMEA, bringing the total to six:

North America

us-east4 (N. Virginia)us-central1 (Iowa)

northamerica-northeast1 (Montréal)northamerica-northeast2 (Toronto)

EMEA

eu-west3 (Frankfurt)eu-west4 (Netherlands)

Based on these developments and Converge’s expert engagement, we have seen a tremendous increase in customer adoption for IP4G.

“Infor was one of the original IP4G subscribers, and years later, we continue to run mission-critical IBM Power workloads in IP4G for our clients. IP4G’s availability and performance have more than met our requirements, and we are extremely satisfied with our overall IP4G experience.” - Scott Vassh, Vice President, WMS Development

Are you thinking of moving your IBM Power workloads to the cloud? For questions and information regarding custom cloud plans, please reach out to power4gcp@googlegroups.com. Your email is private to Converge and Google Cloud representatives, who will follow up with you. Looking for a bit more information first? Check out our data center migration and mainframe modernization solution webpages.

Migrate your SQL Server databases using Database Migration Service, now GA

Mon, 30 Sep 2024 16:00:00 +0000

Enterprises are modernizing their IT infrastructure with managed cloud services to boost reliability, security, and cost-effectiveness. Cloud SQL for SQL Server, a fully managed database offering native SQL Server features as well as AI-powered Google Cloud innovations, is a popular choice for such modernization projects. So today, we are excited to announce that SQL Server migrations from on-premises and other clouds to Cloud SQL for SQL Server are now generally available as part of Database Migration Service (DMS).

Database migrations are often challenging and require scarce expertise. Since we announced SQL Server migrations in Database Migration Service in April, we have received positive feedback from customers who have leveraged Database Migration Service to migrate their SQL Server workloads to Cloud SQL for SQL Server. Database Migration Service uses continuous native backup and restore technologies to minimize the complexity associated with database migrations, providing high fidelity while minimizing downtime, so your critical SQL Server databases can remain operational during the migration process.

Database Migration Service’s unique approach brings the following benefits to your SQL Server database migrations:

Minimal downtime and system overhead: Leverage continuous native backup and restore technology to keep your source databases running while the migration progresses.
Serverless simplicity: Eliminate the need to manage infrastructure, allowing your teams to focus on strategic initiatives.
Security at the forefront: Support for encrypted SQL Server backups ensures robust protection throughout the migration.
No additional charge: Available at no extra cost, making cloud migration even more accessible.

For gaming company Plarium Global Ltd, Database Migration Service’s SQL Server migration capabilities have helped it easily migrate to Google Cloud.

“We needed a migration solution for our large, active SQL Server instances that prioritized business continuity and ease of use. DMS was the answer, enabling high-speed, low-downtime migrations with an intuitive interface that simplified advanced configurations such as combining different backup types for improved migration performance. It was the perfect fit for our needs.” - Omer Niv, CTO, Plarium

Getting started with Database Migration Service

Here’s how to start migrating your SQL Server workloads today using Database Migration Service:

1. Navigate to the Database Migration area of the Google Cloud console, under Databases, and create a Connection Profile pointing to a Cloud Storage bucket to which your SQL Server backup files will be uploaded.

2. Similarly, create another Connection Profile pointing to your chosen Cloud SQL for SQL Server instance.

3. Create a migration job to connect both connection profiles and choose which databases you’d like to migrate.

4. Test your migration job and make sure the test was successful as displayed below. Then start the job whenever you're ready.

5. Upload a full backup and continuously upload transaction log backups for each database to the Cloud Storage bucket at your desired frequency.

6. Once the full backups have been restored, the migration phase changes from “Initial Load” to “Incremental Load”. Database Migration Service keeps looking for new transaction log backup files and continuously restores them.

7. Monitor the progress of your migration jobs using the built-in metrics and logs to assess the right moment to complete the migration.

8. When ready to complete the migration, click “Promote” for Database Migration Service to recover the databases, making them available and completing the migration.

That’s it! Your new Cloud SQL for SQL Server database is ready to go.

Start your SQL Server migration today

With Database Migration Service’s simplicity, security, and minimal downtime capabilities, get ready to modernize your SQL Server database infrastructure and unlock the full potential of the cloud. To learn more, check out Database Migration Service.

Get paid to migrate to a modern data platform with BigQuery and Dataproc incentives

Wed, 24 Jul 2024 16:00:00 +0000

Enterprise data platforms are undergoing a significant evolution to meet the changing needs of companies that are building generative AI applications. While traditional data platforms were designed for specific tasks, like data warehousing or data science, generative AI requires an evolution towards improved data quality, scalability, accessibility, and governance, along with tightly integrated gen AI model execution, to unlock all of your data’s full potential and drive innovation. Migrating to a modern AI ready data platform is paramount for organizations to be successful in the AI era.

Google Cloud’s unified data platform is designed to meet today’s AI needs. Together, BigQuery and Dataproc provide a path to data modernization with built-in capabilities for data lakehouse architectures including multimodal data, multi-engines (SQL, Spark, Python), streaming, governance and integrated ML/AI capabilities. With superior price-performance across data workloads at any scale, organizations can run all of their workloads, delivering best in class ROI and faster time to value.

Customers like Veo have transformed their ability to access, understand, and use data by migrating to Google’s Data Cloud, with 30% lower total cost of ownership and data processing costs. At Veo, BigQuery and Looker have become the heart of their machine learning operations by making it easy for teams to create, deploy, and manage models at scale. Seattle Children’s Hospital migrated 650+ databases to BigQuery to build a sustainable growth strategy to serve business demands for AI/ML, natural language processing and image analytics. General Mills completed a data lake migration to Google Cloud 30% faster than planned which helped them offer new capabilities and streamlined data management practices. By migrating to Google Cloud, including moving from Hadoop to Dataproc, Choreograph, a WPP company, saw over a 50% decrease in cloud costs.

However, migrating to a cloud-based platform is not a trivial undertaking: It requires running two systems in parallel during the migration period and, when coming from another cloud, data egress fees are incurred on top of costly implementation services.

To help organizations accelerate time to value with data and AI, we are excited to introduce a Data Platform Migration incentives program targeted at data warehouse and data lake migrations from on-premises and clouds, by new and existing Google Cloud customers. The program makes it simpler, easier and more cost effective than ever to migrate workloads to an AI ready ecosystem. In addition, our enhanced migration services tooling makes these journeys more seamless than ever.

Migration incentives

The Data Platform Migration Incentive Program provides significant benefits to organizations looking to modernize their data foundations, accelerate their gen AI readiness, unlock the full potential of their data, and drive innovation and growth:

Google Cloud credits: Offset 1st year costs of running two systems in parallel while migrating.
Implementation credits: Enjoy a smooth transition with implementation credits that can be applied to Google or eligible partners.
Cloud egress credits: Credits to cover egress costs associated with moving data from AWS or Azure to Google Cloud (up to $250k).

Migration services and tools

BigQuery migration service provides a comprehensive set of tools to migrate data into BigQuery, so you can run analytics at scale and build an integrated data-to-AI platform. With services such as the BigQuery migration assessment, Batch SQL translator and the Interactive SQL translator, you can migrate your data quickly, efficiently, and with minimal downtime. BigQuery migration service is now being expanded to cover migrations from Hadoop & Spark into BigQuery and Dataproc. New features in the 2024 roadmap include automatic assessment of on-prem Hadoop clusters and automatic table migration for open table formats such as Iceberg. Usage of BigQuery migration services tools has grown by over 400% YoY, with thousands of customers using these services to migrate from other providers during this period.

Build your unified, AI-ready data platform with BigQuery and Dataproc

Google Cloud offers several advantages to organizations exploring new generative AI initiatives with BigQuery and Dataproc:

Built-in intelligence: Integration with AI capabilities, including Gemini and many out-of-the-box ML/AI models, enables organizations to leverage AI to accelerate their business outcomes.
All data and workloads in one platform: Support for a diverse range of data formats and workloads makes Google Cloud a single destination for an ever-growing number of use cases and all your data needs.
Simplicity at its finest: BigQuery and Dataproc’s serverless architecture and unified UI simplify data management and analysis for users of all skill levels.
A pocketbook-friendly powerhouse: With cost-effective data processing, streaming, and governance capabilities, BigQuery and Dataproc offer a lower TCO compared to alternative cloud-based enterprise lakehouse solutions.

With new migration service tools and the Data Platform migration incentive program, you can begin enjoying these capabilities, and get started on your generative AI journey. To learn more, contact your sales representative today, or if you’re new to Google Cloud, use the contact form here. Get ready to embark on a transformative data journey today.

Beam moves to Google Cloud to drive efficacy of government safety net programs

Tue, 28 May 2024 16:00:00 +0000

At Beam, we're building the next-generation digital social safety net. Our platform is used by state and local governments, community based organizations, and universities to administer public benefits and cash assistance programs. Our partners are able to drive low-friction application processes, tackle time-consuming eligibility determination processes, and disburse critical payments to vulnerable populations.

Recently, we migrated our entire infrastructure from AWS to Google Cloud with no downtime or impact to our users. Additionally, we transformed aspects of our application and engineering processes along the way to benefit from the newest cloud technologies. We are more effective and greener — all while cutting our cloud bill in half. In the following post, we’ll review Beam's migration strategy, what we expected to get out of our move to Google Cloud, and some of our key results and takeaways.

Architecture decisions

When migrating to Google Cloud, Beam's overall architectural strategy was to choose the services that would most effectively accelerate the integration of product-differentiating technologies, provide a scalable yet easy-to-understand cloud infrastructure, and introduce minimal administrative burden on the engineering team.

We want our engineers to focus on building a user-centered product with the features that our government partners need instead of supporting the outdated and inefficient infrastructure they used previously. This priority drove our decision to adopt Google Cloud services.

Cloud Run

Beam's platform consists of many containerized apps that need to interact and independently scale to deliver services to users. The platform used to run in self-hosted AWS Kubernetes clusters, but the engineering team found that the administrative burden of the clusters was holding them back. Using AWS to implement Kubernetes didn’t provide the ease of management and developer-friendly UI that we needed to scale.

This is where Google Cloud’s managed container offerings really shine. Cloud Run was the perfect fit for our small team, which wanted to deploy many highly scalable, interrelated container services with the least administrative burden possible. If we needed more flexibility down the road, we could easily redeploy our services to GKE. And since Cloud Run now supports sidecars and multi-container services, there was no reason not to adopt this fully managed container service.

AlloyDB for PostgreSQL

Beam uses PostgreSQL for our core databases, and AlloyDB for PostgreSQL, Google Cloud’s fully managed PostgreSQL-compatible database, seemed like the perfect fit for our needs. Even though AlloyDB was a relatively new service when we began our migration, we wanted a highly scalable database with the ability to replicate across zones and regions for disaster recovery and high-volume reporting traffic — and AlloyDB fit that bill. Additionally, AlloyDB’s Query Insights dashboard seemed like it would close the database administration (DBA) gap and help us tune the performance of our application. Finally, we wanted a database product that didn’t require hand-holding and dedicated DBAs to manage, which would allow our teams to focus on innovation.

ML and AI services

The accelerated pace of improvement around machine learning and AI technologies has generated both intense excitement and trepidation. If implemented responsibly, AI stands to reduce the time tax and revolutionize the government's ability to serve its people. Done carelessly, however, AI could amplify harm and bias and leave vulnerable populations further behind.

In this challenging environment, Beam’s responsible AI strategy is enabled by cutting-edge Google Cloud AI, such as Vertex AI. Our AI offerings aim to radically reduce administrative burden on applicants and overhead on program administration by streamlining the eligibility determination process from documents and case histories. Google Cloud enables us to spend more time on responsibility and equity considerations than we could otherwise.

The migration

I’ve done around half a dozen major infrastructure migrations in my career, and Beam’s migration from AWS to Google Cloud was the smoothest. In part, this was thanks to Beam’s awesome engineering team, but also due to how easy Google Cloud makes a cloud-to-cloud migration.

Here’s a summary of how we did it:

We stood up all our infrastructure with Terraform so everything would be documented, repeatable, and compliant going forward.
Using Database Migration Service, we created continuous replication from our PostgreSQL databases in AWS to AlloyDB instances.
Meanwhile, our developers focused on modernizing our service architecture with things like Pub/Sub for asynchronous job queueing and Cloud Storage for file uploads.
While we were migrating to Google Cloud, we rebuilt our DevOps processes around GitHub.
We used GitHub Actions to deploy our container services to Cloud Run as part of our new CI/CD pipelines.
We ran load tests on our Cloud Run services to ensure they would scale while keeping latency low for our end users.
On cutover day, we briefly paused writes to our databases, and we switched our DNS from the AWS endpoints to the Cloud Run URLs.
Due to the continuous data sync between our previous databases and AlloyDB, our users experienced no outages or data loss during the switch, and for us at Beam, it was a huge relief to be running our application in a more lightweight yet modern cloud platform.

Key results and takeaways

Six months later, we are thrilled with the performance of our platform rebuilt around Cloud Run and AlloyDB. With these improvements, we are on track to distribute hundreds of millions of dollars across program areas, including workforce development, child care, disaster recovery, guaranteed income, and housing.

Cloud Run just works — services scale up and down to match traffic patterns, and our deployments are easy to manage with GitHub Actions and container service definitions stored in yaml. When manual intervention is needed, for example, to route traffic between different application revisions, the Cloud Run UI is vastly superior to the container orchestration tools we previously used.

AlloyDB feels like having a team of DBAs working to keep our database healthy. Latency is shockingly low, even as our business has grown. With a few clicks in the console, we created a separate read-only pool for our data analytics and reporting needs, and we restored a point-in-time backup to a new cluster to retrieve a previous version of user data. And by purchasing committed use discounts, we’ve cut our database costs in half for the next three years.

Some more benefits we’ve seen include the following:

Security: Google prioritizes security in the areas important to Beam and its government partners. For example, default encryption at rest across all Google Cloud services, easy keyless service accounts with Workload Identity, and Security Command Center are some of the features that empower a small but security-focused engineering team.
Developer-friendly: Our developers find Google Cloud documentation easier to understand, and SDK integrations are straight-forward and quick compared to other cloud providers.
Innovation-enabling: Our cross-functional team was able to build effective AI models for pilot partners on top of Cloud Run and other services.
Socially-conscious and environmentally responsible: We’re proud to have Google Cloud as our strategic cloud partner for non-technical reasons too. Google Cloud’s sustainability strategy is the most ambitious of all the key cloud providers, and their philanthropic initiatives align with Beam’s mission and with the values of our partners and investors.

Most importantly, we’ve seen all these benefits for us and our partners while reducing our total monthly cloud costs by half!

Learn more about how Google Cloud can help your startup, and unlock resources at the recently launched Startup Learning Center.

How Facturation.net migrated Azure VMs to Google Cloud in one weekend

Tue, 21 May 2024 16:00:00 +0000

Facturation.net is a leading Canadian provider of a cloud-based invoicing and billing software. With many customers in Canada, its mission is to help doctors and their administrative staff with their medical billing and provide customized software and services.

“As our business grew, we realized that our existing Azure infrastructure was no longer meeting our needs. We were facing increasing costs and complexity, and we wanted to find a more scalable and cost-effective solution,” said Jacob Verret, CTO at Facturation.net. “After evaluating several options, we decided to migrate our production VMs to Google Cloud.”

The challenge was figuring out how to migrate Facturation.net production virtual machines (VMs) in Azure over a single weekend without any impact to our production workloads. The company needed a solution that was simple, reliable, and cost-effective.

After researching its options, Facturation.net decided to use Migrate to Virtual Machines (M2VM) on Google Cloud.

M2VM, which is part of the Google Cloud Migration Center, allows you to migrate virtual machines from VMware, AWS, and Azure environments to Compute Engine in a few minutes. With M2VM, you can migrate VMs safely, at high velocity, and at scale.

M2VM is designed to be simple and easy to use. It has a user-friendly interface and requires no special skills or expertise. The service provides several benefits that make it a great choice for cloud-to-cloud migrations, including:

Fast and efficient migration: M2VM uses a high-performance data transfer engine that can migrate VMs quickly and efficiently.
Proven reliability: Google Cloud customers have used M2VM to migrate hundreds of thousands of VMs from on-premises and cloud environments.
Enterprise-grade security: M2VM uses industry-standard security protocols to protect your data during migration.
Cost-effective: As a managed service, M2VM is a cost-effective solution that can help you save money on your cloud migration. It has no additional costs on top of those associated Google resources.

Migrating Azure VMs to Google Cloud

The Facturation.net team used the following step-by-step approach to migrate their VMs using M2VM:

The team created a detailed migration plan that included a list of the VMs to migrate, the target Google Cloud project, and the desired migration start and end times. They also made sure that the VMs were properly prepared for migration, including having the latest security patches installed and disabling any unnecessary services.
They enabled M2VM by creating a migration project in the Google Cloud console, added an Azure source, and defined the target project on Google Cloud for the migration process.
To start the migration process, the team selected the VMs to be migrated and the target Google Cloud project. M2VM then created a replication job for each VM. They monitored the progress of the replication jobs in the M2VM console.
Once the replication jobs were complete, they tested the migrated VMs to make sure they were working properly and then performed a cutover to switch traffic from the Azure VMs to the new Google Cloud VMs.
After the transition was complete, the team cleaned up the Azure resources that were no longer needed.

Fast, flexible, and safe migration to Google Cloud

“We were very happy with the results of our migration using M2VM. The process was straightforward, and the intuitive user interface allowed us to migrate our VMs without any problems. On top of that, it was incredibly fast — we were able to migrate all of our production VMs over to Google Cloud in a single weekend,” said Alexander Grange, senior cloud architect at Facturation.Net, who led the migration effort.

In addition, Facturation.net did not experience any data loss or corruption during the migration process, and since M2VM uses industry-standard protocols, the team was confident that the company’s data was safe and secure during the migration process. The cost of migration was also very reasonable, allowing Facturation.net to save money on its cloud migration.

Overall, thanks to Migration to Virtual Machines and the Google Cloud team, Facturation.net was able to move rapidly from Azure to Google Cloud — without impacting production.

Modernization vs. migration for data workloads

Wed, 24 Apr 2024 16:00:00 +0000

Cloud migration involves moving digital assets like data, apps, and infrastructure to a cloud environment. In particular, data and data platforms are essential for application, analytical, or AI/ML workloads; thus, it’s common to migrate existing data from other platforms, including on-premises or even other cloud providers. For most organizations, determining what to do with their data when moving it to a new platform is the first challenge.

Should you do a simple migration, also known as lift-and-shift? Or should you consider modernizing the platform and building an entirely new solution that can work with the latest modern cloud technologies? In this post, we will discuss the modernization and migration approaches for data workloads.

Migration vs. modernization: What’s the difference?

Generally speaking, migration involves moving your data to the cloud. The most common migration processes typically fall into two categories:

On-premises to cloud migration: Shifting operations from local servers and data centers to cloud providers.This is a common migration use case, as businesses look to take advantage of cloud benefits like scalability, flexibility, and cost savings.
Cloud-to-cloud migration: Switching between cloud providers to gain additional value in terms of innovation, functionality, cost, and partnership.

Cloud modernization, on the other hand, is a strategy for transforming existing applications, data, and infrastructure to take better advantage of the benefits offered by cloud computing. Having a unified, flexible, scalable and cost effective data platform for managing and analyzing data is a crucial part of any company’s success. For example, fully managed database services can enhance the performance, scalability and reduce the overall cost of operation in the cloud. Cloud-first data lakes and data warehouses enable modern analytics capabilities, providing better insights into customer data.

Typically, customers consider cloud modernization when their business strategies strive to achieve digital transformation, leverage advanced cloud services, support continuous improvement and development, and unlock the potential of emerging technologies like generative AI.

You might consider cloud modernization, for example, if you are looking to:

Rethink and rebuild applications and infrastructure using cloud-native architectures like serverless data platforms, microservices, and containerization.
Utilize managed services like serverless databases, data lakes, machine learning platforms, analytics tools, and cloud operations to automate tasks, unlock new functionalities, and drive faster time-to-market.
Adopt DevOps principles and CI/CD practices to iterate and optimize your cloud environments.
Provide a strong foundation for developing and deploying your gen AI use cases with scalable data management, sufficient computing power, and pre-built AI services.
Reduce cost, improve security and compliance posture, and increase reliability & productivity via managed offerings vs self managing databases

Critical migration decision factors

It can be a challenging and sometimes overwhelming process for customers to decide whether to migrate or modernize — not to mention what to migrate.

That being said, on-premises databases can be difficult and expensive to scale in most instances. As a result, migrating and modernizing databases to cloud-based services can provide improved scalability for handling large data columns and help guarantee better performance through on-demand computing resources. In addition, cloud solutions offer better governance and security options, and the reduction of the operation costs can have a significant impact on the company.

The development and deployment of new solutions can also benefit from cloud-native CI/CD processes. Providing a centralized cloud environment for multiple teams makes it easier to overcome data silos and break down limitations that hinder cross-team collaboration, which drives higher productivity and less error during development and deployment cycles.

So, how do you decide the best approach for your organization’s data workloads?

We have found that the following six factors can be helpful for organizations:

Company strategy: Company strategy plays a decisive role in shaping migration strategies. It’s important to carefully consider how factors, such as mergers and acquisitions, geographical expansion, and vendor lock-in, impact your cloud requirements before making a final decision about migration processes.
Cost considerations: While owning and maintaining data centers involves significant capital expenditure, cloud hosting can also result in unexpected fees without proper oversight into provisioning and resource usage. In particular, data egress fees should be taken into account, especially when moving large amounts of data across clouds.
Security and compliance: The complexity of the security models and compliance requirements are important factors for determining migration strategies. Local and government rules and regulations like HIPAA, GDPR, or PCI can have a major impact on how a migration process will work for different types of workloads.
Performance and reliability: Migrating to the cloud comes with expectations for nominal downtime and high reliability on cloud resources, but you should always understand the latency, scalability, and downtime requirements for services you plan to migrate. Most cloud services provide three, four, or five digit (99.9% or 99.99% or 99.999%) availability SLAs, but there are still scheduled downtimes and maintenance windows that could be a factor for migration planning.
Features and functionality: As a company grows, its cloud needs might evolve. Over time, you might need additional features, services, or scale to support business expansion or seek out specialized services to stay competitive.The innovations and new technologies offered by cloud services should be carefully assessed when making migration decisions.
Support and customer service: Migrations come with a lot of unknowns and a steep learning curve. Knowing that there is sufficient support and customer hand holding during the migration process can help ensure a successful migration.

Best practices for planning your modernization or migration project

Migrations have been occurring in enterprise IT for decades. As a result, most organizations are familiar with migration or modernization processes (e.g., mainframe to x86 servers, SQL workloads to NoSQL, Hadoop framework adoptions, and many more).

When planning your own projects, it can be useful to frame your approach according to the following four categories:

Opportunity evaluation:Identify and quantify the opportunity and business case, then begin driving stakeholder alignment. Activities like due diligence and discovery can offer insights into the overall IT landscape to properly map out the various cloud-based options.
Foundation and planning: A fundamental key in a successful migration or modernization project is to set a solid foundation for cloud infrastructure. Determine and prioritize which workloads are ideal to migrate to the cloud and plan on how to migrate them.
Migrate and validate: Execution, validation, and retiring old systems are all essential steps in migration projects. Start with the workloads that are most ideal for the cloud and then plan subsequent projects based on business value, technical priority, and cloud-propensity. Analyzing and evaluating at each step enables fine-tuning for upcoming migrations.
Optimize and operate: After migration, you will need to operate and optimize the workloads you moved to a new environment. Adopting cloud-based operations to monitor and understand workload performance is a very important step in any cloud migration process. Customers who don’t go through this process can easily become overwhelmed with the cloud cost and complicated cloud environments.

Modernization and Migration Loop (images created by Gemini)

Regardless of the process, migration approaches tend to follow a similar pattern and share similar characteristics. For instance, we’ve found that it’s important to highlight people, process and technology elements when planning projects — all key factors that can contribute to a successful migration. Here are some examples to consider for each element:

People: Aligning stakeholders, building skills, and managing resources
Process: Harvesting best practices, tracking processes through a governance model, or defining and monitoring well-defined OKRs
Technology: Identifying technical gaps, prioritizing migration workloads, and building proper project landing zones with adequate financial, security and compliance controls

A well-run migration process creates a flywheel effect, where learnings and experience gained from migrating workloads compound to refine the business case, bolster the foundation, accelerate subsequent migrations and make it easier to optimize workloads over time.

Drawing on the article's insights and my own field experience, I strongly recommend considering a modernization path for your data workloads if possible. Serverless technology offers compelling benefits that can significantly reduce your TCO and boost the ROI of modernization efforts. These benefits include cost savings, enhanced security, improved performance and scalability, access to the latest technologies, and increased agility and innovation. By choosing modernization, you can quickly realize these substantial gains, just like one of our many customers did.

Google offers a Database Modernization program to de-risk your migrations by offering end to end guidance and support with tools, resources, best practices and incentive funding. Please visit this link for more information and to apply for the program.

Thank you for reading! Have a question or want to chat? Follow me on LinkedIn and Twitter.

Your scannable list of our top migration announcements from Next ‘24

Thu, 11 Apr 2024 16:00:00 +0000

Regardless of what you’re looking to migrate or modernize, Google Cloud is committed to helping you on that journey, and we’re excited to announce a number of new updates for our portfolio of migration products and programs.

What’s new with the Rapid Migration Program (RaMP)?

We understand that customers’ journeys to the cloud are driven by the need to unlock the value that cloud computing offers, from cost-savings, efficiency gains and scalability to catalyzing innovation. And to help customers fully realize the value of cloud, RaMP now covers migration and modernization use cases that span across applications and the underlying infrastructure, data and analytics. RaMP now has pillar programs including RaMP for Databases, RaMP for Mainframe Migration and RaMP for Storage to provide customers with prescriptive guidance for specific workloads .

This ensures Google Cloud and our partners consider the holistic view of customers’ workloads, and provide a roadmap that allows customers to take full advantage of cloud-native services like serverless and containerization. Recently we also announced a range of partner incentives to accelerate those customer migration and modernization outcomes across our product portfolio that include our GenAI offerings.

In addition to these program enhancements, Google Cloud also offers comprehensive first-party managed services and third-party tools (via Marketplace) to analyze, plan and execute cost effective migration and modernization projects efficiently.

What’s new with Migration Center?

A key part of RaMP is our unified platform, Migration Center, which helps you accelerate your end-to-end cloud journey from your current on-premises or cloud environments to Google Cloud. With features like cloud spend estimation, asset discovery of your current environment, and a variety of tooling for different migration scenarios, we were excited to share some excellent new additions:

Migration use case navigator lets you map out exactly how to migrate your resources (servers, databases, data warehouses, etc.) from on-prem and other clouds directly into Google Cloud. This provides you with a complete, prescriptive plan with options that you can review, consider, and confirm.
New Cloud Spend Estimators give you a rapid TCO assessment of on-premises VMware and Exadata to Google Cloud VMware Engine (GCVE) and Big Query, respectively.
Database discovery & assessment lets you map out migration plans for MSSQL, PostgreSQL and MySQL to Cloud SQL. For each source database, you’ll get detailed information on technical fit with shape recommendations for Cloud SQL, and a thorough TCO report.

What’s new with migrating my VMware workloads?

Google Cloud VMware Engine, an integrated VMware service with the highest availability SLA, helps you easily migrate or host VMware workloads within Google Cloud with all the hardware and licenses included, announced some exciting new features and functionality:

First cloud provider to announce intent to support VMware Cloud Foundation License Portability, which means broader access to on-premises customers that are looking to bring their VMware Cloud Foundation license and use it for transforming their VMware estate in Google Cloud.
General Availability of larger instance type (ve2-standard-128) offerings that feature 1.8X vCPU, 2.7X RAM and 1.3X storage compared to ve1 and 200 Gbps east-west connectivity.
Advancements in networking including next-gen VMware Engine Networking, automated zero-config VPC peering, and Cloud DNS for workloads.
Terraform Infrastructure as Code Automation for GCVE: Google Cloud VMware Engine now supports additional Terraform resources for automating private cloud, cluster, and network management. These enhancements to the VMware Engine Terraform provider enables full environment Infrastructure-as-Code for your VMware Engine Environment.

What’s new with migrating my virtual machines and disks?

Whether you’re looking to migrate one application from on-premises or one thousand enterprise-grade applications across multiple data centers, Migrate to Virtual Machines gives any IT team, large or small, the power to migrate their workloads to Google Cloud. Here’s what we announced:

Disk Migration offers a robust solution for migrating disk volumes, rather than VMs, to Google Cloud with confidence and ease, serving use cases such as self-managed database migrations, OS resbuild and more.
Image Import (preview) as a managed service within our ‘Migrate to Virtual Machines’ product, which will reduce friction for customers and accelerate their import journeys, as well as improve usability and supportability.
BIOS to UEFI Conversion (Preview) which automatically converts bootloaders to a newer format (UEFI) as part of the migration, which helps ensure that migrated VMs can leverage the full spectrum of Google Cloud’s security enhancements.
Amazon Linux Conversion (Preview) makes it easy to convert Amazon Linux to Rocky Linux in Google Compute Engine.
CMEK Support lets you use Customer Managed Encryption Keys (CMEK) support within Migrate to Virtual Machines, letting you maintain control over your own encryption keys, further securing your migration to Google Cloud.

What’s new with replatforming my virtual machines to containers in GKE or Cloud Run?

The new Migrate to Containers (M2C) CLI is purpose-built for application owners, for fast and easy modernization of application components that run on VMs using your local machine. The CLI generates artifacts that you can deploy to GKE or Cloud Run. The offline mode enables the use of the CLI locally without pulling resources from the internet during runtime.
We also announced a preview of our M2C Cloud Code Extension, which provides an integrated experience to migrate applications from VMs to containers running on GKE, directly in Visual Studio. The extension provides a guided replatforming journey, technical fit assessment, and automated artifact generation to run existing applications on GKE, and helps boost developer productivity.

What’s new with migrating my databases?

Start migrating databases in just a few clicks with a single, integrated conversion and migration experience. Reduced migration complexity means you can enjoy the benefits of the cloud sooner. Here are some of our announcement highlights:

Database Migration Service AI-powered last mile code conversion from Oracle (on any platform) to PostgreSQL. Gemini automates the conversion of database code, such as stored procedures, functions, triggers, packages and custom PL/SQL code, for destinations into Cloud SQL for PostgreSQL or AlloyDB. To see it in action watch this video. Plus, explainability (in preview) helps you reskill and retrain PL/SQL developers by allowing side by side comparison of the dialects along with detailed explanation of code along with recommendations.
DMS for SQL Server migrations (preview), a new homogeneous minimal-downtime migration from SQL Server (on any platform) to Cloud SQL for SQL Server.
In Datastream, SQL Server as a source for CDC introduces a seamless, next level data movement to BigQuery destinations.
The new RaMP for database modernization program combines best practices, tools and attractive funding to help customers move from Oracle and SQL Server to Google Cloud databases.

What’s new with modernizing mainframes?

Google Cloud has significantly increased our investment in Mainframe Modernization in order to support our biggest customers running legacy applications on Mainframes and MidRange systems. With a wide portfolio of 1st and 3rd party tooling and key partnerships with delivery and tech partners for a complete end to end modernization solution, we were pleased to announce some great new functionality across the entire mainframe modernization journey (what we’re playfully referring to as our AARDVARK):

Assess - Mainframe Assessment Tool (MAT) now powered by GenAI accelerates your migration journey by analyzing application codebase, performing fit assessment and creating application-level summarization and test cases. Watch this video to see it in action.
Augment - Mainframe Connector sends a copy of your mainframe data to BigQuery for off-Mainframe analytics, which you can see in this video.
Refactor & rewrite - G4 refactors your Mainframe application code (COBOL, RPG, JCL etc.) and data from their original state/programming language to a modern stack (JAVA), or leverages AI to rewrite entirely.
De-Risk - Dual Run lets you run a new system side by side with your existing mainframe, duplicating all transactions and checking for completeness, quality and effectiveness of the new solution. Designed and implemented together with Santander Bank who are using it in production

Partners participating in RaMP can also now leverage the new incentives to drive discovery and modernization of mainframe workloads to Google Cloud Platform.

What’s new with storage migration?

Sometimes you just have a bunch of storage you need to migrate to Google Cloud. To help support that, we’ve got two exciting new announcements as part of the RaMP for Storage pillar program:

Reduced storage egress costs from Amazon S3 to Google Cloud Storage! Our new transfer service moves data via private network (from Amazon S3 to Google Cloud Storage) and results in no egress charges on the AWS side, while a small per GiB Google Cloud service fee still applies. Click here for more information on locations and pricing.
Cloud Storage's client libraries for Python, Node.js, and Java now support parallelization of uploads and downloads, greatly speeding up reads and writes for applications built on top of them. This functionality was available via Google's command-line interfaces, and is now completely integrated into the client libraries, ensuring that Cloud Storage customers get optimal performance regardless of which transfer mechanism they use.

That’s a wrap! What’s next?

Our investment in making your migration and modernization projects fast and easy continues to be a huge focus for Google Cloud. In addition to all of the exciting announcements above, we’ve got so much more to come, especially as we continue to bake generative AI into our migration portfolio. Keep an eye out for more exciting news in 2024! If you are ready to take another step, you can visit our Rapid Migration Program website or sign up for a free discovery and assessment of your existing IT landscape, and then work with our migration experts to map out a plan to get you to the cloud!

IDC: Migrating to Google Cloud IaaS has a 318 percent ROI

Wed, 31 Jan 2024 17:00:00 +0000

Organizations worldwide continue to adopt cloud services to enable and support ongoing digital transformation. While the benefits of cloud infrastructure, including cost savings, agility, elasticity, and scalability are well understood, quantifying these benefits can be challenging.

In late 2023, Google released the results of a sponsored IDC research study to gain a better understanding of the tangible ROI of migrating to cloud infrastructure.The study comprised nine interviews with organizations in the United States, the UK, and Germany across the financial services, government, healthcare, real estate, retail, and utilities industries. The selected organizations employed Google Cloud Infrastructure as a Service (IaaS) and had firsthand experience with its advantages and expenses.

IDC: Extracting more business value from cloud infrastructure

As part of this Google-sponsored research on the business value of Google Cloud IaaS, here are just a few snippets of what the IDC Research team found in the study¹:

318% five-year ROI
51% reduced cost of operations over five years
57% more efficient IT infrastructure management teams
75% faster deployment of new application features
$3.23 million in additional revenue gained

Want to learn more about this IDC research and how organizations are benefitting by adopting Google Cloud IaaS?

Download the full IDC research study.
Attend a fireside chat where IDC research VP Dave McCarthy and Google North American Developer Advocacy Lead and best-selling author Priyanka Vergadia discuss the results of the study in depth.

^{1. Source: IDC Business Value Research, Sponsored by Google, The Business Value of Google Cloud IaaS, IDC #US51037323, August 2023.}

^{With the following firmographics:
- Number of employees: 1,500–300,000
- Number of IT staff: 300– 2,000
- Number of business applications: 20–6,000
- Revenue: $126.3M–$59.0B}

Cloud switching just got easier: Removing data transfer fees when moving off Google Cloud

Thu, 11 Jan 2024 15:00:00 +0000

At Google Cloud, we work to support a thriving cloud ecosystem that is open, secure, and interoperable. When customers’ business needs evolve, the cloud should be flexible enough to accommodate those changes.

Starting today, Google Cloud customers who wish to stop using Google Cloud and migrate their data to another cloud provider and/or on premises, can take advantage of free network data transfer to migrate their data out of Google Cloud. This applies to all customers globally. You can learn more here.

Eliminating data transfer fees for switching cloud providers will make it easier for customers to change their cloud provider; however, it does not solve the fundamental issue that prevents many customers from working with their preferred cloud provider in the first place: restrictive and unfair licensing practices.

Certain legacy providers leverage their on-premises software monopolies to create cloud monopolies, using restrictive licensing practices that lock in customers and warp competition.

The complex web of licensing restrictions includes picking and choosing who their customers can work with and how; charging 5x the cost if customers decide to use certain competitors’ clouds; and limiting interoperability of must-have software with competitors’ cloud infrastructure. These and other restrictions have no technical basis and may impose a 300% cost increase to customers. In contrast, the cost for customers to migrate data out of a cloud provider is minimal.

Making it easier for customers to move from one provider to another does little to improve choice if customers remain locked in with restrictive licenses. Customers should choose a cloud provider because it makes sense for their business, not because their legacy provider has locked them in with overly restrictive contracting terms or punitive licensing practices.

The promise of the cloud is to allow businesses and governments to seamlessly scale their technology use. Today’s announcement builds on the multiple measures in recent months to provide more value and improve data transfer for large and small organizations running workloads on Google Cloud.

We will continue to be vocal in our efforts to advocate on behalf of our cloud customers — many of whom raise concerns about legacy providers’ licensing restrictions directly with us. Much more must be done to end the restrictive licensing practices that are the true barrier to customer choice and competition in the cloud market.

How to properly — and easily — do data disk migrations to Google Cloud

Tue, 31 Oct 2023 16:00:00 +0000

Data disk migration is the process of transferring data from one storage device to another. This can involve moving data between physical devices, virtual machines, or even transitioning from on-premises solutions to cloud-based platforms. The ultimate goal of data disk migration is a seamless transition without compromising data integrity, security, or accessibility. In this blog post, we walk through how to use Migrate to Virtual Machines, which is built into Google Cloud’s Migration Center, for a fast, easy way to successfully migrate data disks to Google Cloud.

Why data disk migration matters

1. Upgrade to a newer OS: An unsupported operating system poses a significant threat due to the lack of official updates, security patches, and technical support. This leaves the system vulnerable to cyber threats, compatibility issues, and regulatory non-compliance, while also hindering innovation and functionality.

Disk migration involves transferring data from the outdated, unsupported operating system to a newer and supported one.

2. Modernization: As technology advances, older storage devices can become bottlenecks, limiting performance and capacity. Data disk migration enables you to take advantage of newer, more efficient hardware.

3. Data protection: Aging storage devices are more susceptible to failures and data loss. Migrating data to newer platforms helps mitigate these risks and ensures its safety.

4. Performance enhancement: Newer storage technologies often come with improved read and write speeds, leading to enhanced system performance and reduced access times for applications.

5. Scalability: Data disk migration facilitates the scalability of your storage infrastructure, allowing you to accommodate growing data demands without compromising efficiency.

Google Cloud Disk Migration

Google Cloud ease the burden of disk migrations with the integration of Migrate to Virtual Machines directly within Migration Center. This robust and effective remedy for disk migration leverages the capabilities of cloud migration technology to seamlessly migrate OS disks or data disks, irrespective of their compatibility with the cloud environment. This is the ultimate solution when you’re facing the challenge of updating your operating system or giving your hardware a boost.

Continuing Google Cloud’s penchant for flexibility and openness, Migrate to Virtual Machines connects with/to a growing number of migration sources, including VMware and other cloud service providers. For instance, consider a scenario where you manage an extensive array of virtual machines, some of which run on old-school Linux distributions or ancient Windows versions (such as W2003, W2008, W2012). Here’s where we see the magic of the disk migration feature of Migrate to Virtual Machines comes into play. You can selectively migrate the source workload disks and subsequently attach them to a supported operating system, effectively modernizing your workload infrastructure.

How it works

Here are the stages in the disk migration process:

First, you need to onboard the VM whose disks you want to migrate.

In the following example we have a VM which is hosted on a local VMware Datacenter; we select it and onboard it to the GCP Migration service:

Then, we can start to replicate it:

Once the VM is onboarded to the Migration service we can immediately start to replicate data from the disks of the source VM to Persistent Disk volumes that are on Google Cloud. The data replication is a continuous process that takes place in the background until the final cutover, or you delete the migration.

You can also leverage Monitoring:

For additional information, you can click on the VM and access a detailed view of the disk replication status, the estimated time-to-completion, and other related details about the disk migration.

While the replication is on-going, you can already go ahead and set the disk’s target details: Configure settings for the migrated disk, such as the Hosting project (Region and Zone), the disk type, and more.

This will allow saving time and ensure that there’s no rush as the cut-over date approaches.

The replication cycles can also be adjusted to synchronize with the cumulative data peak of incremental changes.

Summary

The Migrate to Virtual Machines disk migration feature brings in a fresh capability that lets you smoothly migrate compute disks from any source VM to Google Cloud disk storage. This functionality taps into the seamless, agentless migration feature of Migrate to Virtual Machines, which means there’s no disruption to the source workload, and it comes with perks like regular replication cycles and a quick cut-over time.

This capability expands migration precision to individual disks, leading to innovative uses like easily shifting workloads to a different operating system or transferring entire data stores to a new platform.

Ready to dive in? Head over to Migration Center to start exploring Google Cloud's centralized, end-to-end hub for migration and modernization, which includes Migrate to Virtual Machines. Not quite there yet? Check out this 3-minute video on Migration Center, or sign up for a free IT discovery and assessment with Google Cloud, so you can figure out exactly what your IT landscape looks like and how you can migrate it successfully!

Reshaping Flipkart’s technological landscape with a mammoth cloud migration

Mon, 30 Oct 2023 16:00:00 +0000

More than a third of consumers in India turn to Flipkart for their shopping needs, giving the platform a user base of more than 450 million individuals. With 1.1 million sellers, the platform offers over 150 million different products across 80 different categories and millions of shipments daily.

Flipkart was looking to reshape their technological landscape and future-proof their operations, and did so on Google Cloud. The fruits of this relationship are evident, as they successfully launched two of their pivotal platforms, Flipkart Data Analytics Platform (FDP), and Content Catalog Object Store, on the Google Cloud infrastructure among other things that were migrated to Google Cloud.

The biggest day of the year for Flipkart is the Big Billion Day (BBD) sale, akin to the global shopping extravaganza Black Friday Cyber Monday. Big Billion Day spans September and October, coinciding with India's vibrant festival season. Over the course of multiple days, Flipkart experiences a transaction volume surge of six to eight times the ordinary business-as-usual (BAU) volume.

Migrating large-scale, complex data clusters within a short timeframe

Flipkart’s existing data platform was built on top of open-source big data technologies, with extensive customizations tailored to cater to Flipkart's unique business requirements. This intricate environment was spread across self-managed data centers located in Chennai and Hyderabad. Facing tight schedules for migration within approximately eight months, Flipkart was faced with a range of challenges:

Migrating large-scale Hadoop clusters: Flipkart needed to move substantial Hadoop clusters with significant computational power and memory resources to Google Cloud.
Complex data migration: The migration spanned beyond infrastructure, encompassing the transfer of over 10k facts, journals, and snapshots, along with 15,000 ETL (Extract, Transform, Load) jobs. This intricate operation involved processing a colossal 10PB of data in batch and 2PB of data in near-real-time, every day
Data ingestion layers: To facilitate data ingestion at scale, the team needed to transition from on-premises Kafka to Google Cloud’s Pub/Sub. This intricate operation involved ingesting 130+ billion messages per day, 60 TB of compressed data on a daily basis during regular operations, and 500 TB per day during peak event periods. Furthermore, the process encompassed 3,000 topics, spanning both general data and sensitive Personally Identifiable Information (PII).
Data processing and analysis: Flipkart needed to create a robust processing platform capable of handling a staggering 1.25 million messages per second for real-time analysis. This was done using Dataproc. Additionally, the platform also needed to efficiently process around two petabytes of messages in real-time and ten petabytes of messages daily, in batch mode.
Migrating self-managed Hive: The migration extended to critical user-facing components, including the migration of the existing self-managed Hive to Hive on Dataproc. This transition aimed to service the requirements of approximately 20 teams and a user base of nearly 350 technical users. Remarkably, the team only had four months with which to accomplish this.
Security assurance: Ensuring a robust security posture for both GoogleCloud- and self-managed services was of the utmost importance, and demanded meticulous planning and rigorous implementation. Google security experts collaborated closely with the Flipkart team, conducting a thorough evaluation of all migrated components to verify their security status and evaluate potential risks and solutions. This assessment process utilized frameworks such as security posture reviews.

Navigating these challenges was not undertaken in isolation, as Flipkart collaborated with the Google Cloud team, which shared its expertise throughout the migration. What was initially planned as a pre-BBD lift-and-shift migration, but a subsequent modernization phase post-BBD quickly evolved into a much more intricate and nuanced process. Recognizing the complexity of Flipkart's use cases, a multidisciplinary team comprising more than 85 members from Google Cloud and partner organizations was convened to steer this ambitious migration journey.

Exceeding SLAs with a solid cloud infrastructure

With Google Cloud, the team built 15 tools, each designed to cater to unique requirements and challenges presented by Flipkart's intricate ecosystem. As the migration encompassed various elements, including data ingestion, batch processing, messaging, and more, these utilities served as vital cogs in the migration machinery.

Fine-tuning and performance optimization efforts were relentless, ensuring that the new infrastructure met and exceeded service level agreements (SLAs). Rigorous testing, including several rounds of scale testing, reaching up to five times the BAU volume, solidified the foundation for a smooth execution of the BBD sale event on the Google Cloud infrastructure.

The impact of this migration was transformative. Flipkart completed their data platform migration from on-premises to Google Cloud within just eight months. The migration included the smooth transfer of substantial components, such as Hive clusters with a significant number of vCPU cores and a colossal volume of Google Cloud Storage, processing a massive amount of messages in batch and real time. Not to be overlooked, the migration of the content management store, encompassing 5.4 billion objects, stands as a testament to the scale and complexity of this endeavor.

Future-proofing the company through a journey of transformation

The real-world impact of the Google Cloud infrastructure coming to life became apparent very quickly. Not only did Flipkart successfully execute the BBD sale event with a 7x increase in transactional data over BAU, but the event's smooth execution fostered a sense of trust and confidence among internal and external stakeholders alike. This migration journey was not only a technological feat but also a testament to collaboration, innovation, and the potential for transformational change within the realm of ecommerce.

Looking back, both Flipkart and Google Cloud learned invaluable lessons. The migration served as a real-world stress test, pushing the boundaries of large-scale infrastructure within the JAPAC region. And as industries continue to evolve, Flipkart's migration to Google Cloud stands as a model of successful partnership and technological advancement. It not only highlights the power of collaboration, but also the boundless potential of combining innovative technology and human ingenuity.

If you’re ready to migrate, read more about the Rapid Migration Program (RaMP) by Google Cloud, or sign up for a free IT discovery and assessment, to figure out what your IT landscape looks like and ensure a successful migration.

Migrating an app from AWS to Google Cloud: A startup’s point of view

Mon, 16 Oct 2023 16:00:00 +0000

Editor’s note: In this post AJ Ross, CTO at Fluxon, discusses the key considerations and comparative technologies for startups looking to move from AWS to Google Cloud.

If you're a startup considering migrating from Amazon Web Services (AWS) to Google Cloud, it’s important to understand the technical differences between them. Luckily, modern cloud platforms are to some extent interchangeable. In our experience at Fluxon, if you’ve already decided to move, you’ll be pleasantly surprised by Google Cloud’s advanced solutions.

When migrating, it’s important to understand not just the costs of your application’s workloads, but also how a cloud platform can lower expenditure thanks to technology that is functionally superior or easier to adopt. For this reason, Google Cloud stands out for a range of proven solutions and APIs that offer attractive features, performance, and reliability all at a competitive price point. In some cases, such as Kubernetes and BigQuery, Google Cloud’s offerings are vastly superior to competitors’.

In this article, I will discuss key considerations and comparative technologies for startups looking to move from AWS to Google Cloud. I will also provide a comparison of the two platforms in terms of features, pricing, and scalability. If you’re looking for a “rosetta stone” of translations from one cloud to the other for common full-stack applications, or to see where Google Cloud is vastly superior to the competition, then read on.

Taking control of Identity Access Management (IAM)

It’s no secret that IAM, with its complicated web of users, groups, roles, and permissions, is one of the more difficult aspects of managing a cloud platform. Both AWS and Google Cloud are highly customizable when it comes to IAM granularity, but it’s important to understand their different approaches.

A major difference you’ll notice with Google Cloud is that there is first-order support for dedicated service accounts. These are effectively users under which your (and Google Cloud’s) programmatic APIs operate. While top-level Google Workspace users are dedicated to human-driven authentication and actions, service accounts are expected to do all of the programmatic work. Google Cloud service accounts provide the benefit of least privilege by creating fine-grained access control. Service accounts are used so extensively in Google Cloud that service accounts are created automatically to run services for you that you tune to isolate roles and servicess, which saves time and effort.

In AWS, nearly every action is governed by a policy and users, which belong to groups and roles, and each action can be checked against a policy. This is different from Google Cloud where, in addition to having generalized roles and policies for each user, resources can also define which users can access them.

For example, on AWS you might create a new user that can access your production database and S3 buckets. To do this you generate a policy that grants access to specific actions on these resources for the new user. On Google Cloud, you create a user or service account, but you set the permissions for that user on the bucket itself. AWS used to have resource-specific policies for S3 buckets, but they have been deprecated for some time.

Where should my application run?

Most of the applications we build at Fluxon are web-based, so determining where and how to deploy our applications is a crucial question. At a basic level, both AWS and Google Cloud offer virtual machines through EC2 and Compute Engine. Both platforms enable you to create VMs by configuring your CPU, RAM, and attached storage parameters. Both platforms also offer opportunities to reduce costs with reserved and spot instances, and both offer VPCs to isolate services securely.

If you’re looking to move your existing VM-based workload to Google Cloud from AWS, you’ll find that the offerings are very similar, with the notable advantages that Google Cloud provides Migration Center for a seamless end-to-end experience, as well as a more modern and holistic interface that can make managing your resources much easier.

However, managing servers adds operational overhead for engineering teams. For this reason, most people start with container-based deployments. Cloud Run is a great solution if you’re migrating a container from an AWS Elastic Container Service (ECS) pipeline, such as ECS Fargate. Cloud Run enables you to host and scale any container easily, and with Cloud Build, you can quickly build a pipeline that deploys new versions of your application.

If you have a workload with microservices or multiple services that need to interoperate, you’ll find that Google Kubernetes Engine (GKE) is best-in-class. GKE is the only managed Kubernetes service that can offer scaling up to 15,000 nodes and delivers multiple types of automation. Unlike the competition, Google Cloud has made it easy to use both Cloud Run and GKE, letting you move between them as needed, without forcing your teams to choose.

If you already have a Kubernetes application on AWS, moving to Google Cloud should be straightforward since there is feature parity in terms of deployment, monitoring, availability, and command-line tools. But with Google Cloud you also have the advantage of Google Cloud console, which provides a cleaner UI for Kubernetes management compared to AWS, which is a bit more fragmented.

Deciding on a database

Both AWS and Google Cloud offer nearly identical options for transactional databases. Both provide fully managed PostgreSQL, MySQL, and SQL Server that are attractive to developers. Both support VM configurability, replication options, and availability zone and replication options for redundancy. But with Google Cloud, you can manage access control through the Identity and Access Management system, so database access can be cleanly integrated with Google Cloud instead of managing users and roles individually on each database.

If you’re looking for lightweight or ephemeral storage such as Memcache or Redis, Google Cloud offers a Redis API-compatible service called Memorystore that is perfect for caching or other high-speed key-value set operations you would expect from Redis. However, keep in mind that Memorystore doesn’t offer persistence. While Memorystore’s more-expensive Standard tier utilizes multiple high-availability replicas, your data isn’t flushed to disk anywhere. This should be fine in theory, but make sure to understand the difference before fully switching to Memorystore.

One area where Google Cloud excels is with reporting databases. If you have a data lake in AWS, you’ll be pleasantly surprised by BigQuery, which is a cost-effective solution for storing massive amounts of data that can be queried with SQL. BigQuery enables you to stream records to it (similar to Kinesis), store them in its own proprietary format (like Redshift), and you can use it to scan external data sources (like Spectrum or Athena).

To migrate data from AWS, check out the Google Cloud Storage Transfer Service (for cross-cloud synchronization) or their Transfer Appliance, a high-capacity storage device that enables you to transfer and securely ship your data to a Google Cloud facility, where your data is uploaded to Cloud Storage. If you already have a business intelligence and analytics platform, it most likely supports BigQuery, but if you don’t you can try out Looker Studio (free) or subscribe to Looker Studio Pro.

Cloud Storage is analogous to AWS S3 — both are cost-effective platforms where you can store and serve objects. However, the pricing options vary on both platforms according to storage class, region, redundancy, data transfer amount and direction, and access frequency. In terms of functionality, many of the Cloud Storage features are very familiar, and Cloud Storage supports the S3 protocol. You can import data from other providers by using the “gsutil” command line utility or with the Storage Transfer Service.

Over to you

Depending on the complexity of your applications, migrating from AWS to Google Cloud could be easy and straightforward, or it could involve a lot of complexity and require a lengthy transition. While many of Google Cloud solutions should be directly translatable from AWS, the hard part is always in the details, such as access control, networking, and other platform idiosyncrasies. In the meantime, I hope this guide has been useful and given you a broad overview of how to approach a transition from AWS to Google Cloud. And while many services are similar, Google Cloud stands out for their easier, more modern interfaces and their stronger security controls.

If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.

Get to ‘migrate now’ with optimized migration waves from CAST Highlight

Mon, 02 Oct 2023 16:00:00 +0000

Cloud migration offers many benefits, including enhanced scalability, cost-efficiency, improved security, flexibility, and speed. However, to realize the full potential of Google Cloud, one needs a pragmatic approach that allows applications to become cloud-friendly during their migration and throughout their lifecycle on Google Cloud.

Therefore, it is important to build application and workload migration waves that also take into account the internal characteristics and underlying infrastructure of the workloads that you want to migrate.

CAST Software’s CAST Highlight is a software intelligence product that analyzes source code to provide insights into a portfolio of applications. It automatically categorizes all applications in various modernization strategies like rehost, refactor, rearchitect, retire; recommends a migration roadmap; identifies cloud blockers and sizes remediation efforts. It assesses applications on various parameters such as software health, elegance, agility, cloud readiness, technical debt, green impact and resiliency.

In this blog, we outline how to use CAST Highlight to build optimal application migration waves to Google Cloud, for quick wins, modernization, and effective collaboration between cloud competency centers and application teams.

Wave 1- Quick wins: Migrate now with minimal effort and risk

The first wave of the migration to Google Cloud aims to identify and implement quick wins, or low-hanging fruit. These applications, despite being less risky to migrate, can still provide tangible benefits in a relatively short period of time. By focusing on applications with relatively low business impact, high cloud readiness scores, and small refactoring effort, businesses can ensure a smooth transition to Google Cloud in weeks rather than months.

The first wave will also serve as a pilot to test various aspects of the migration process, such as procedures, the selected landing zone, and collaboration between teams. This allows practitioners to familiarize themselves with the process, identify potential challenges, and refine the project plan for subsequent waves.

In other words, the first wave is a way to test the waters and get a feel for the migration process before moving on to larger and more complex applications. This will help to ensure that the migration is successful and that businesses can reap the benefits of Google Cloud as quickly as possible.

Wave 2 – Core cloud: Migrate next while modernizing now

This phase involves a new set of applications with a high impact on the business and a high readiness score for the cloud. During this phase, detailed insights and lessons learned from wave 1 applications are shared with application teams. This gives developers the opportunity to proactively refactor and remediate applications as they assess their cloud readiness with CAST Highlight, rather than undertaking a new project or bringing in a new team.

Wave 3 – Long term: Migrate first, modernize next

In this phase are applications that have a high impact on the company but require a significant amount of money and effort to make them cloud-native. A tactical and immediate move to the cloud for some workloads (e.g., distributed applications like Java, .Net, etc.) is a wise decision, as it allows businesses to quickly reap some of the cloud's benefits, such as data analytics capabilities, savings on recurring infrastructure costs. Preparation for the as-is move can begin as early as wave 1.

Wave 4 – Pursue later: Modernize first, migrate next

The fourth wave of cloud migration involves applications that need to be modernized before they can be migrated. This is particularly true for applications that fall into the "platform-dependent non-x86" category or have specific data replication challenges. Applications such as mainframe systems, IBM Power, and Oracle SPARC servers can benefit from a custom-fit modernization journey based on application insights from CAST, in order to align with cloud-native architecture and capabilities.

Conclusion

After the initial migrations and modernizations are completed, organizations can focus on the practical aspects of continuous modernization throughout the lifecycle of applications hosted in the cloud, including the use of Google Cloud tools and services for ongoing monitoring and optimization.

Looking to get started? With Google Cloud's holistic cloud migration and modernization program, Google Cloud RaMP (Rapid Migration Program), you can easily gain insight into your current landscape and estimate the total cost of migration with complimentary assessments from Google Cloud. Get a quick assessment on where you are in your cloud journey or sign up for a free, more comprehensive assessment of your IT landscape.