Modernizing a legacy data lake into a cloud-native AWS lakehouse
A legacy data lake had grown complex, fragile, and difficult to operate, limiting the team’s ability to scale analytics and ensure data trust.
Context
A legacy data lake had grown complex, fragile, and difficult to operate, limiting the team’s ability to scale analytics and ensure data trust.
Problem
- High operational overhead and frequent pipeline failures.
- Lack of clear governance, data quality guarantees, and metadata.
- Slow iteration and limited support for real-time use cases.
Approach
- Redesigned the platform around a cloud-native, serverless AWS lakehouse architecture.
- Introduced clear separation between ingestion, transformation, and consumption layers.
- Established governance and quality as core platform capabilities.
Key decisions
- Adopted Apache Iceberg as the table format to enable schema evolution and reliable incremental processing.
- Used managed and serverless AWS services to reduce operational burden.
- Designed batch and streaming pipelines under a unified architectural model.
Result
The new platform significantly improved reliability, operability, and trust in analytics, while enabling both batch and real-time workloads.
What I learned
- Early investment in governance and observability prevents long-term platform fragility.
- Serverless architectures simplify operations but require strong architectural discipline.