Case Study: Streamlining Data Operations with a Metadata-Driven Data Lakehouse on Azure

Our client fortune 500 FMCG company faced challenges with their existing Databricks data lake solution, characterized by complexity, duplicated datasets, and a lack of structure. They sought a solution that would simplify their data operations, improve data quality, and enhance data discoverability while optimizing costs.

Our team embarked on a transformative project to migrate the client’s current Azure Databricks data lake solution to a metadata-driven Azure Databricks data lakehouse using medallion architecture. Leveraging cutting-edge technologies and industry best practices, we designed and implemented a solution that would enforce a medallion structure, streamline data pipelines, and improve data quality.

Utilizing Databricks, Python, Azure, and Spark, we designed a metadata-driven data lakehouse with a medallion structure, ensuring clear organization and discoverability of data. We developed a state-of-the-art Python framework to handle pipeline loads, incorporating industry best practices and features such as automatic data extraction using metadata, automatic data archiving, and incremental loads support.

One of the key features of our solution was the enforcement of proper medallion structure without forcing users to change their code writing style, promoting ease of use and adoption. Additionally, we integrated a built-in data quality tool, Great Expectations, to ensure data integrity and reliability.

The migration to a metadata-driven data lakehouse yielded significant benefits for our client. The clear data structure and medallion organization facilitated data discoverability and enabled citizen developers to work directly with datasets, promoting self-service analytics and innovation.

Furthermore, the implementation of automatic data extraction, archiving, and incremental loads support reduced pipeline costs and improved operational efficiency. The integration of Great Expectations enhanced data quality, ensuring that data meets predefined expectations and standards.

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