Streamlining Data Operations with a Metadata-Driven Data Lakehouse on Azure
Challenge
A Fortune 500 FMCG company struggled with their existing Azure Databricks data lake solution, which was plagued by complexity, duplicated datasets, and a lack of structure. They required a streamlined solution to simplify data operations, enhance data quality, and improve data discoverability, all while optimizing costs.
Our approach
Our team launched a transformative project to migrate the client’s Azure Databricks data lake to a metadata-driven data lakehouse using the medallion architecture. By leveraging Databricks, Python, Azure, and Spark, we implemented a scalable and organized solution that enforced the medallion structure and improved data quality without disrupting user workflows.
Key components of the solution included:
- A metadata-driven framework for data pipeline automation, incorporating features like automatic data extraction, archiving, and incremental load support.
- Seamless medallion structure enforcement that maintained user-friendly flexibility.
- Integration of Great Expectations for automated data quality checks and validation.
The outcome
The migration to a metadata-driven data lakehouse delivered substantial improvements in data discoverability and usability. The medallion architecture provided a clear structure, enabling citizen developers to directly engage with datasets, fostering self-service analytics and innovation.
Additionally, automation features, including data extraction, archiving, and incremental loads, significantly reduced pipeline costs and enhanced operational efficiency. Integrating Great Expectations ensured data integrity and reliability, meeting high-quality standards.