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6 Steps to Google Cloud Migration and Critical Best Practices

Read Time: 7 minutes

Why Do Organizations Migrate to Google Cloud?

Google cloud migration refers to the process of moving digital operations into the Google Cloud platform. This includes transferring data, applications, and IT processes from on-premises servers or other cloud environments to Google’s cloud infrastructure. 

The drivers for migrating workloads to Google Cloud typically include:

  • Supporting digital transformation by gaining access to the latest technology and infrastructure
  • Scaling resources up or down as needed without investing in physical hardware
  • Leveraging Google Cloud’s advanced analytics and machine learning services
  • Savings costs and transitioning from capital expenses to operating expenses with Google’s pay-as-you-go pricing model

6 Steps to Google Cloud Migration

According to the official Google Cloud Migration Guide, these are the recommended steps for migrating to Google Cloud:

1. Assessing and Discovering Workloads 

The first step is to create a thorough inventory of existing IT assets. This includes identifying all applications, data stores, and services currently in use and determining their compatibility with Google Cloud. Understanding the technical requirements, dependencies, and performance metrics of each workload helps in planning a successful migration.

Workloads should then be categorized based on their complexity, criticality, and cloud readiness. This helps in prioritizing migration efforts and selecting the most appropriate migration strategies for each workload. It also aids in estimating costs and identifying potential challenges early in the migration process. 

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2. Building a Foundation in Google Cloud

Preparing a target environment for the migration involves setting up a scalable and secure cloud environment that aligns with the organizational structure and compliance requirements. This includes creating Google Cloud projects for different teams or applications, implementing access controls, and configuring network settings to ensure secure connectivity between on-premises systems and the cloud.

Next, it’s important to establish a consistent logging and monitoring framework to track performance and security across all cloud resources. This helps maintain visibility into operations, optimize resource usage, and identify potential issues early.  

3. Transferring Large Datasets 

Moving large datasets to Google Cloud requires strategic planning to minimize downtime and ensure data integrity. Google Cloud’s Storage Transfer Service can be used to move large volumes of data across different storage services, whether from on-premises servers or other cloud platforms. This service automates the transfer process, supports scheduling for recurring transfers, and provides data security measures during transit.

For extremely large datasets or scenarios where network constraints make online transfers impractical, Google’s Transfer Appliance offers a viable solution. This high-capacity server is shipped to the customer’s location, where data can be loaded and sent back to Google for direct upload into Google Cloud Storage. This reduces the time required to move terabytes or petabytes of data.

4. Deploying the Workloads 

The next step is to transfer applications and services into the cloud environment and configure them for optimal performance. This requires a detailed execution plan that outlines the migration of each workload, including any necessary modifications to ensure compatibility with Google Cloud’s architecture. 

It’s crucial to conduct thorough testing during this phase to validate that all systems operate correctly in their new environment, ensuring that there are no disruptions to business operations. After successful deployment, it’s important to monitor workloads closely to assess their performance in the cloud. 

Google Cloud’s monitoring tools can help track usage, identify potential bottlenecks, and optimize resource allocation. Continuous monitoring also provides insights for future scalability and improvements. 

5. Migrating to Automated, Containerized Deployments

Switching from manual deployments to automated, containerized deployments simplifies the process of managing and scaling applications. By leveraging Google Cloud’s container services, such as Google Kubernetes Engine, organizations can automate the deployment, scaling, and management of their applications. 

Containers encapsulate an application’s code, configurations, and dependencies into a single object, ensuring consistency across different environments and simplifying development and operational tasks. Automated deployments through continuous integration/continuous deployment (CI/CD) pipelines further enhance efficiency by enabling automatic build, test, and deployment phases for code changes.  

6. Optimizing the New Environment 

Optimizing the Google Cloud environment involves continuous assessment and adjustment of resources. Google Cloud’s operations suite can be used to monitor performance metrics and identify underutilized or overprovisioned resources. Adjusting compute instances, storage allocations, and network configurations based on actual usage patterns can reduce costs. 

Organizations can implement auto scaling to dynamically adjust resources in response to workload changes. Managed services within Google Cloud can reduce the operational burden by automating infrastructure management tasks, while serverless computing options support running code in response to events without provisioning or managing servers. 

Related content: Read our guide to cloud migration checklist

Free Migration Tools Offered by Google Cloud

Google Cloud Platform offers several free tools that can help organizations migrate their systems.

Migrate to Virtual Machines (formerly Migrate for Compute Engine) 

Migrate to Virtual Machines, previously known as Migrate for Compute Engine, enables the transition of workloads from on-premises or other cloud platforms directly into Google Cloud’s infrastructure. It automates many of the tasks involved in moving VMs to Google Cloud. It supports a range of source environments, including VMware vSphere, Microsoft Hyper-V, and physical servers.

The tool also optimizes VMs for Google Cloud, ensuring the best possible configuration for performance and cost efficiency once migrated. This includes converting VMs to appropriate machine types within Google Cloud, setting up network configurations, and ensuring security compliance.  

Migrate to Containers 

Migrate to Containers helps move applications from traditional or virtualized environments into containerized deployments on Google Cloud, utilizing services like Google Kubernetes Engine (GKE) for orchestration.

This process begins with containerizing the application’s components, ensuring they are packaged with all their dependencies, which simplifies deployments across environments. Once applications are containerized, they can be managed and scaled using GKE, which automates tasks like deployment scaling, updates, and management of containerized applications across clusters of hosts.  

Database Migration Service

Google Cloud’s Database Migration Service (DMS) simplifies the process of migrating databases to Google Cloud, supporting various database types including MySQL, PostgreSQL, and SQL Server. This fully managed service automates migration tasks such as data replication, validation, and synchronization without requiring significant downtime. 

DMS ensures a secure and reliable migration pathway by providing continuous data replication with minimal performance impact on the source databases. This can accelerate the migration process while reducing the risk of data loss or corruption. The service offers a straightforward setup process and interface for managing migrations. It provides monitoring capabilities that allow users to track the progress of their migrations in real time.  

BigQuery Data Transfer Service

BigQuery Data Transfer Service automates data movement from SaaS applications to Google BigQuery, enabling the integration of analytical data into a centralized warehouse. It supports transfers from advertising platforms like Google Ads, campaign managers, and external sources such as Amazon S3. 

This service simplifies the process of aggregating and transforming data for analysis, eliminating the need for custom scripts or manual uploads. It enables scheduled transfers that ensure data freshness without constant monitoring. Its automatic schema detection and dynamic adjustment reduce errors and save time during data ingestion. Built-in error reporting and retry mechanisms improve reliability.  

Best Practices for GCP Migration

Here are some of the measures that organizations can take to improve their migration strategy to Google Cloud.

Establish a Rollback Strategy 

Having a rollback strategy for each step of the migration plan ensures that any issues encountered during the migration can be quickly addressed without significant impact on business operations. This involves creating detailed documentation for each phase of the migration, outlining the steps to revert changes if necessary. 

For example, before migrating a database, take a snapshot or backup. If the migration encounters issues, systems can revert to this snapshot, minimizing downtime and data loss. Test the rollback procedures to ensure that if a rollback is needed, the team is prepared and understands the exact steps to follow. Monitor the migration to quickly identify when something goes wrong and trigger the rollback process, automating as much as possible.  

Plan for Gradual Rollouts and Deployments 

Gradual rollouts help minimize risk and ensure stability during migration to Google Cloud. Instead of moving all workloads at once, this involves migrating small, manageable groups of resources or applications in phases. By doing so, teams can closely monitor the impact of each phase, addressing any issues before proceeding further. 

This phased approach allows for the adjustment of strategies based on real-world performance data. Use Google Cloud’s deployment tools and services to support versioning and traffic splitting, enabling the redirection of a portion of user traffic to the new cloud-based resources while keeping the rest on the existing infrastructure. Gradually increase traffic to the cloud environment as confidence grows. 

Prepare the Development and Operations Teams 

Effective communication strategies should keep all stakeholders informed about migration schedules, potential impacts, and any required actions on their part. This includes setting up channels for real-time updates during critical phases of the migration and establishing protocols for reporting issues. 

Keeping these teams in the loop helps foster collaboration, allowing them to quickly address challenges. It’s also important to provide development and operations teams with training and resources related to Google Cloud technologies, including regular briefings. This ensures that team members can manage and optimize cloud-based environments post-migration.  

Define How to Retire the Source Environment 

It’s important to have clear criteria for safely retiring the source environment, ensuring that all data and applications have been successfully migrated and are fully operational in the Google Cloud environment. This includes verifying the integrity of data transferred, that all applications are running as expected, and that there are no dependencies on the old environment. 

Conduct comprehensive testing in the new cloud environment to validate performance and functionality against predefined benchmarks. Thoroughly review and update documentation to ensure that all operational, configuration, and troubleshooting documentation reflects the new cloud-based systems accurately. 

It’s also important to confirm that backup and disaster recovery mechanisms are in place and operational in Google Cloud before decommissioning the source environment. Finally, obtain formal approval from all stakeholders based on these criteria to officially mark the readiness for retiring the source infrastructure. 

Streamline Deployment of Workloads to the Cloud 

Ensuring the ability to update and deploy each workload involves establishing a CI/CD pipeline that supports automated testing, building, and deployment of workloads to Google Cloud. By integrating source control management tools with Google Cloud’s deployment services, teams can automate workflows, making updates more reliable and efficient. 

Additionally, adopting infrastructure as code (IAC) practices allows for the consistent deployment of cloud resources, minimizing manual errors and speeding up the migration process. 

Containerizing workloads where possible provides a uniform environment for applications across different development stages and production environments, enabling smoother updates and deployments.

Conclusion

Migrating to Google Cloud can offer substantial benefits, including increased scalability, advanced analytics capabilities, and cost savings. By following the outlined steps and best practices, organizations can ensure a smooth transition and maximize their return on investment. Whether leveraging Google’s migration tools, implementing phased rollouts, or establishing robust monitoring and rollback strategies, careful planning and execution are key to a successful cloud migration.
Learn more about Faddom for data center migration or try it yourself with a free trial

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