How to Migrate Legacy Databases to AWS

Database migrations to the cloud are becoming increasingly common. Many organizations are looking to boost performance, security, and agility, especially in the age of big data and AI/ML. The good news is that today’s cloud platforms, like Amazon Web Services (AWS), are making such improvements easier and easier.

However, the actual work of migrating databases to the cloud is easier said than done. Some companies must account for thousands of on-prem servers, dozens of data centers, and huge full-stack applications. There is a lot that can go wrong and many ways that organizations are at risk when making the move from on-prem to cloud or from cloud to cloud.

In this post, we’ll discuss the stages involved in most database migrations, along with how ClearScale approaches these projects. By the end, you’ll have a better understanding of what it takes to be successful when migrating legacy databases to the cloud, and what options are available to you when considering a cloud database migration.

The Four Database Migration Stages

Most database migrations consist of four stages:

• Migration design
• Migration preparation
• Migration execution
• Database workload maximization

Each stage is essential to the short-term and long-term success of database migration projects.

Stage 1: Migration design

At this stage, the current architecture of databases and applications is evaluated. Service windows are identified, and the overall application availability is considered. This is crucial as it serves as the foundation of further migration and ensures continuous application operation during the migration phase, or helps minimize the downtime needed for all procedures to be finalized without any data loss.

Also, during this stage, the dependencies are identified that may need to be modernized. This may include wringing new configurations or provisioning new storage types, refactoring code or purchasing additional licenses, ensuring compliance, and providing capabilities for emergency rollback. All these factors must be carefully researched before moving forward.

Stage 2: Migration preparation

Migrating legacy databases requires significant preparation for several reasons. First, unlocking the full benefit of the cloud is difficult without certain capabilities. Engineering teams must have an adequate understanding of how to handle things like database management, security, networking, DevOps, and API development on the cloud, specifically. On-prem skill doesn’t always translate directly to cloud sophistication.

Legacy applications and databases also often need to be refactored to make them easier to maintain as the business evolves. Lift-and-shift migrations are typically faster than those involving any sort of modernization work. Still, they often cost more in the long run when you factor in the time, money, and energy needed to keep these non-refactored databases updated on the cloud.

Furthermore, engineering teams have to choose the right database migration strategy for their needs. This involves evaluating existing dependencies, understanding current spend, projecting TCO, and creating a clear roadmap. A tool like AWS Database Migration Service can be valuable here for automating a lot of the Discovery work in this stage. AWS also offers a Schema Conversion Tool (SCT) that makes it easy to convert existing schemas for new database engines.

Stage 3: Migration execution

Even with sufficient preparation, database migrations can be long and cumbersome. Engineers must ensure high availability and zero downtime while in transition. Data can’t be lost, stolen, or compromised, which is hard to guarantee with rapid data transfers.

Furthermore, organizations have to be able to navigate a stretch of time in which they are split between their source and target databases. Depending on the specific combination of technologies in play – whether it be Microsoft SQL Server and Amazon Aurora, Oracle and PostgreSQL, or some other pairing, staying focused and organized isn’t easy.

Fortunately, solutions like the Amazon Database Migration Accelerator ease the transition by guiding engineers through key technical decisions and workflows. The Amazon Database Migration Accelerator gives IT teams access to AWS database experts and only charges at the conclusion of the project based on a fixed price model.

Stage 4: Database workload maximization

Getting to the cloud is only part of the answer when it comes to database migrations. Engineering teams still have to do work to maximize their database workloads. The cloud offers immense flexibility, scalability, and availability, but these advantages are only available to those who configure their databases and applications properly.

According to the application requirements, you should use the variety of database engines and approaches that AWS offers. You can opt for large pre-provisioned data grinding machines or choose robust and elastically scalable offerings for flex consumption. From industry-standard SQL-based warehouses to modern NOSQL solutions built for various purposes. It is imperative that you make the choice while looking at the entire application.

On AWS, data engineers can choose from 15 database engines, including managed database solutions. They can add robust monitoring and logging for database architecture, on top of analyzing usage closely to identify potential optimization opportunities. Services like AWS Cost Explorer, AWS Trusted Advisor, Amazon S3 Analytics, and Amazon CloudWatch are tremendously useful here for measuring results once on the cloud.

Migrate Legacy Databases to AWS with ClearScale

Given that database migrations can be a complicated and lengthy process, it often makes sense to work with an AWS Premier Tier Services Partner, like ClearScale, on such projects. ClearScale has over a decade of experience with the AWS cloud, which includes countless database migrations and modernizations.

When migrating legacy databases to AWS, our team follows an 8-step process:

1. Assess current infrastructure
2. Develop a migration and rollback strategy
3. Design high-level architecture
4. Build a proof of concept
5. Configure disaster recovery
6. Refactor back-end applications
7. Test performance
8. Document results

This methodology has worked time and time again for clients across a wide range of industries.

We recently worked with a learning and performance management business that wanted to migrate from a self-hosted PostgreSQL database to AWS. Our engineers took the time to understand the client’s business requirements, developed a robust database migration plan, and then executed the migration with no disruption to the organization. As a result of migrating to AWS, the company now spends far less time managing databases and has more capacity to explore innovative opportunities.

Want to learn more about how ClearScale can help your organization migrate legacy databases to AWS? Download the eBook:

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