Data modernization is a key priority for enterprises seeking to leverage a fast-evolving digitized economy and a surge of AI-driven innovation.
According to analysts Gartner, 35% of data center infrastructure will be managed from a cloud-based control plane by 2027, from less than 10% in 2022.1
Migrating legacy data is particularly appealing as that can reduce licensing costs and total cost of ownership (TCO) while enabling businesses to leverage the cloud.
Data is easier to integrate when it resides in the cloud, thus creating the seamless ecosystem needed for Generative AI plans or advanced analytics deployments.
Writes Sammy Tam in InfoWorld: “Regardless of company size, establishing continuity and reliability of the organization’s data mobility functions is a vital undertaking, and selecting the correct approach and solution for data migration is essential.”2
Yet migrating legacy data poses significant challenges. Relational Database Management Systems (RDBMSs) that have grown organically over decades can suffer with poor documentation while key business functionality may not be understood.
What’s more, many legacy RDBMSs feature embedded server-side business logic; a pattern less pervasive in modern cloud-native data stores. Migrations involving this software requires specialist skills many enterprises don’t possess in-house.
Indeed, many firms will underestimate the complexity, leading to projects running over budget. The problem is exacerbated by a lack of end-to-end migration tools, with the most capable also the most complex and expensive.
As David Campbell, Head of the Google Cloud Architecture Practice for UK and Ireland at EPAM Systems explains: “Your documentation about how you’ve developed the systems could be lost or no longer up to date, and the experts who managed them and developed that business logic on them are no longer available.
“You’re trying to understand that logic retroactively, and often questioning whether it’s even valid or not. In the context of a large enterprise, it can be challenging.”
Migrating legacy data demands a change of skills and mindset, as development teams move from simply building systems to running them in production. This additional responsibility can be highly demanding.
Accelerating the process and removing risk
Enterprises can address these challenges by working with a partner who can help them through the process, providing the tools, skills and expertise to simplify and de-risk the pathway, end-to-end.
EPAM has years of experience in modernizing legacy RBDMSs, utilizing modern software patterns to abstract any business logic and functions.
Its MigVisor tool provides clear metrics to assess existing systems, isolate potential compatibility issues as well as provide information on predicted timescales and costs.
These include not just the TCO of the migration process, but also the future costs of running in the cloud.
As Campbell notes: “It accelerates the process; you can go in fast and not wasting time going down a blind alley.”
Working with EPAM’s cloud-database experts and software engineers ensures that enterprises solve any problems that arise during migration fast.
Bugs in migration tools or a quirk of a cloud native database don’t cause substantial delays. And as the project nears completion, knowledge sharing leaves the in-house team better equipped.
“We don’t want to parachute in, handle the changes and leave the team wondering how they do things” says Campbell. ‘We work together with them – we may even give them formal training – and when we leave, they can feel confident and self-sufficient.”
Find out more about how working with EPAM could accelerate your database migration.
1Gartner, Gartner Says Four Trends Are Shaping the Future of Cloud, Data Center and Edge Infrastructure, May 2023 https://www.gartner.com/en/newsroom/press-releases/2023-05-16-gartner-says-4-trends-are-shaping-the-future-of-cloud-data-center-and-edge-infrastructure
2InfoWorld, Understand the 3 major approaches to data migration, May 2023, https://www.infoworld.com/article/2338398/understand-the-3-major-approaches-to-data-migration.html