Database migration and modernization, from legacy databases to managed platforms

Database modernization moves a database off legacy or self-managed technology onto a platform that is easier to run and scale, such as Amazon RDS, a managed relational database service with automated maintenance, encryption and failover. It often reshapes the data too, so reporting, analytics and AI services can use it. Consolidation is a common form: Helvetia's 52 Microsoft Access databases became one SQL Server database.

Database migration moves data from one database or platform to another with its integrity intact. Database modernization changes what the data runs on or how it is structured: a managed service in place of a self-managed server, a new engine, or one database in place of many. Most modernization projects include a migration; not every migration modernizes anything.

Rehosting moves a database as it is to new infrastructure, also called lift-and-shift. The same approach works beyond databases: Aavas Financiers moved 35+ TB of legacy document data to AWS in a phased lift-and-shift migration. Replatforming keeps the application's design but moves the database onto a managed service such as Amazon RDS. Refactoring changes the engine or schema, such as converting Oracle to Aurora PostgreSQL. Mainframe modernization moves VSAM, DB2 and IMS data stores onto managed AWS database services. The free modernization assessment recommends one path per application.

Our database pages name MongoDB, MySQL, PostgreSQL, Firebase, Oracle, SQL Server, DynamoDB and Amazon RDS. We move Oracle, SQL Server and aging data warehouses onto Amazon RDS, Aurora and Redshift. In published projects we have consolidated Microsoft Access databases into SQL Server for Helvetia and moved a consent management platform to MongoDB when its load outgrew the existing database.

On AWS, migrations use AWS Database Migration Service (AWS DMS) and the AWS Schema Conversion Tool (AWS SCT), planned for minimal downtime. Large moves run in phases: Aavas Financiers' 35+ TB went to AWS in a phased lift-and-shift migration, and at EHCC databases were moved with rollback paths. Helvetia's case study records that the migration was carried out smoothly, with minimal disruption to its operations.

There is no single answer: it depends on how many databases you have, how large they are and how clean the data is. The free modernization assessment includes a modernization calculator view of cost, effort and timeline, so you get an estimate before anything moves. For Salesforce data migrations, a straightforward migration from one well-maintained source system with under 50,000 records takes two to four weeks, and migrations from multiple systems with significant data quality issues take four to ten weeks.

Yes. HabileLabs, an AWS Advanced Tier Services Partner that holds the Amazon RDS Service Delivery validation, builds Multi-AZ architectures, migrates databases with AWS DMS, sets up encryption and backups, and monitors production databases 24/7 for SaaS, healthcare and other application teams. A Service Delivery validation covers one AWS service and requires an AWS audit and Advanced Tier status first. HabileLabs is also a Microsoft Solutions Partner for Azure Data & AI.

It builds the foundation, but it does not make data AI-ready on its own. Data quality, governance, lineage and access controls still need their own work. Our data engineering services cover that side: data quality and governance frameworks, integration and ETL pipelines.