Implement a metadata-driven delta lake framework for file processing

Background

Our client is a government organization in Belgium. The mission involves migrating from an outdated data vault to a delta lake framework using a modern ETL approach. This will enable the client to handle growing data demands, streamline operations, and support strategic decision-making with improved data accessibility.

Challenge

• Integrating new data sources into the existing data vault was a time-consuming process due to poor documentation and limited understanding of the framework. • All new development occurred directly in production, due to the lack of a deployment system, resulting in frequent downtime and delayed reporting. • The client faced scalability issues, preventing them from meeting increasing data volume and complexity demands effectively.

Approach

We transitioned from the legacy data vault to a modern delta lake framework utilizing an advanced technology stack, including Python, Azure Synapse, and Azure SQL Database. This metadata-driven process eliminates the need for manual adjustments with each new data source. A robust deployment pipeline was introduced to manage separate environments, supported by an automated testing framework to validate code changes prior to deployment. Training sessions and documentation were also provided to ensure the internal team could maintain and expand the framework effectively.

Key results

Implement an automated test framework to minimize production downtime.

Develop a scalable, metadata-driven ETL framework with a modern technology stack to improve data quality and reporting.

Reduce ETL framework complexity, enhancing team efficiency and scalability.

Benefits

• Faster reporting and enhanced data quality through streamlined processes. • Increased team productivity with reduced manual effort. • Improved scalability and adaptability to accommodate future data growth and complexity.