Best Practices to Follow When Migrating to Google Cloud Platform

When it comes to migrating to the cloud, organizations today can choose multiple approaches like hybrid, multi-cloud, and more. One of the common trends observed with cloud migration over the past years is how organizations can quickly migrate to Google Cloud with ease. 

Google Cloud Platform (GCP) is one of the most popular and preferred choices for organizations looking to migrate to the Cloud today – Google’s Infrastructure as a Service (IaaS) solution provides multiple benefits to individuals and organizations alike. But migration is not a “one size fits all” solution; organizations must follow the best practices while migrating to the Cloud to reap its benefits.

Why GCP?

While various cloud platforms are available in the market today, GCP stands out with its unique benefits and advantages. GCP supports virtual machines similar to AWS and Azure; however, it focuses more on modern, containerized workloads and the capability to deploy ML models.

GCP offers instant and elastic scaling as and when required, in-memory storage, support for real-time feeds and analytics, integration with business intelligence tools, support for open-source technologies, high security, adherence to compliance, reliable and fast global network that ensures high-performance, and exceptional user experience – all of which makes it the preferred choice.

Best practices when shifting to GCP

Many organizations often miss an essential point: Cloud migration is not completed in a single step; it is a continuous process, and downtime is a part of it. A successful migration is interruption-free – it doesn’t affect the ongoing operations and reduces downtime as much as possible.

In our years of experience helping businesses shift to the Cloud, we’ve compiled a list of best practices to consider when migrating to GCP:

Assessing the Legacy Platform

Legacy data platforms have been built over many years and are extremely diverse and complex to comprehend. No individual or team can clearly articulate its various ETL patterns, technology tool stack, deviations from standards, data lineage, technical debt ingrained, and so on. In-depth assessment of the platform helps analyze the applications that need to be eliminated and those that can be migrated after restructuring. It helps the IT teams quickly discover the major differences between the legacy platform and GCP. The legacy platform can have different operational models compared to a public cloud, including networking, physical security, hardware, power, and virtualization standpoint. Having a clear understanding of the characteristics and quirks of the legacy platform is an absolute must before embarking on a GCP migration journey. 

Planning the Migration

The importance of planning the migration cannot be stressed enough. Following an iterative approach of migrating distinct workloads with minimal external dependencies is the most followed best practice. A well-planned process for Change Management – both at the application and user level is very critical for the success of the migration. The planning stage needs to consider business priorities, application criticality, and workload complexities and should be based on the logical outcome of a scientifically performed legacy platform assessment. An automatic migration planner provides accurate budget, time, and cost estimates.

Architecting for the Cloud Platform

Many organizations take the path of porting their legacy platform as if they are deploying the application for a Data Center managed by the cloud vendor. Relying on IaaS is a proven anti-pattern for the cloud. This is a recipe for failure with higher operating costs, limitations in scalability, and an architectural debt that will take years to resolve. Designing for the Cloud requires an appreciation of the long-term and continuous benefits derived from the use of Cloud-native services, fully managed platforms, and true serverless offerings. 

Code Conversion Techniques

The variety and volume of legacy code in itself can become an insurmountable challenge. While it is important to modernize the code to tailor it to the Cloud environment, it is also not wise to over-engineer and introduces program-level risks. A fine balance needs to be struck between code re-engineering and code leveraging. Furthermore, a code conversion approach that relies majorly on manual efforts is an invitation for trouble. The introduction of automation and standardized conversion practices will help in keeping the migration effort at manageable levels.

Managing Risk

Legacy platforms serve a critical business role in the organization and re-platforming it can be a potential source of business risk. A well-thought-out and articulated QA and Operationalization strategy will go a long way in significantly mitigating risk. Goals like field-level data comparison and automation are some of the critical components of a risk-averse QA strategy. Special focus on migration tasks like History Data Load, Catch up, and Cutover help in mitigating operational risks.

Conclusion

Better security, higher uptime, high productivity, flexibility to embrace changes and hybrid workloads, and saving significant time and resources are many reasons to choose GCP. But GCP migration comes with its own set of challenges – like unexpected compatibility, missing documentation, data loss, or miscommunication with stakeholders.

Datametica is a certified Google Cloud partner who has helped many businesses successfully migrate to the Cloud. Our Cloud Data migration tools / product suite automates migration, reduces costs and risks, and accelerates the migration timeline. Reach out to us today to start your GCP migration journey.