Legacy Data Platform Modernization on AWS

phData's Legacy Data Platform Modernization offering is a repeatable, use-case-agnostic path for organizations running business-critical analytics on legacy data warehouses and/or aging ETL tools.

These estates are typically tightly coupled, hard to test, and hard to change. Logic is locked in proprietary stages, stored procedures, and custom routines, and the platform depends on scarce legacy skills and costly licensing. They also limit what comes next: analytics and AI initiatives need a governed, flexible data foundation that legacy platforms struggle to provide.

phData moves these workloads to the modern cloud data platform that fits your needs on AWS-native, Snowflake on AWS, Databricks on AWS, etc.. Transformation logic is rebuilt in code-based frameworks such as dbt, with orchestration on Amazon MWAA (Apache Airflow), or another scheduler selected during design. Pipelines become modular, version-controlled, testable, and easier to evolve. The target platform is selected during assessment based on your workloads, skills, and cost goals.

Step 1 - Migration Assessment. This is the entry point and can be purchased on its own. Using the phData Toolkit, we inventory jobs, stages, source connections, embedded SQL, and custom routines, then tier every workload by complexity. You receive a sized, sequenced migration plan, a target architecture, and a clear view of effort, risk, and dependencies.

Step 2 - Migration and Modernization. A typical implementation includes and not limited to:

Data movement: bulk historical loads into your target platform, with change data capture (CDC) to keep data current while the legacy platform continues to run. Ingestion tooling is selected based on the source database platform and version. File-based ingestion: Amazon S3 as the landing zone, with native cloud ingestion (for example Snowpipe, external tables, or Auto Loader, depending on the target) replacing FTP and sequential-file stages. Transformation: legacy jobs converted to dbt models or equivalent platform-native code, with custom routines individually ported, replaced, or retired. Orchestration: workflow and sequence logic rebuilt as DAGs in Amazon MWAA or dbt Cloud, selected during design. Migration in waves: a POC/MVP/first use cases, then bulk conversion of simple and medium workloads, followed by complex and very complex workloads (migration sequence will vary depending on customer’s priorities, timelines, legacy EDW renewals, etc.. Validation and cutover: automated data validation, parallel running across at least one full business cycle, and cutover against agreed acceptance criteria.

Engagement length depends on estate size and complexity. Large estates are typically planned in phases, with timelines confirmed after assessment sign-off. phData provides architecture documentation, runbooks, and knowledge transfer so internal teams can operate and extend the new platform, including as the foundation for analytics and GenAI workloads on AWS.