Design Recommendations and Guidelines for Adopting Unity Catalog in Your Existing Azure Data Lakehouse + FAQ

In this blog post, my aim is to guide you on how to effectively utilize the Unity Catalog within your current data lakehouse and offer my recommended best practices. These practices are designed to help you fully leverage the new features of the Unity Catalog without causing disruptions to your existing data architecture. Additionally, I will outline the recommended design patterns for new Data Lakehouse implementation using Managed Tables including an FAQ section for clarity.

Description of Existing Data Lakehouse Architecture: Legacy Method Using Mount Points

Over the past few years, I have worked with various clients in the Azure space who have embraced and implemented the Medallion architecture for their enterprise Data Lake. This architecture typically involves having at least three different Azure Data Lake storage containers (bronze, silver, gold) to store various stages of data refinement. Databricks is commonly used in conjunction with a data lake to ingest, process, and write data back to the data lake containers or zones. In this architecture, it is common to create Databricks mount points against the different data lake zones.