Our team is planning a massive migration of legacy Spark jobs to the Databricks Lakehouse platform. What are the common pitfalls we should avoid regarding Unity Catalog integration and workspace organization to ensure we don't end up with a messy governance structure on day one?
3 answers
The biggest hurdle is moving from a traditional HDFS mindset to the Lakehouse architecture. Start by implementing Unity Catalog early for centralized access control across all your workspaces. Avoid "Lift and Shift" for your code; instead, take the time to refactor RDD-based logic into DataFrames or Spark SQL to leverage the Photon engine's acceleration. Also, ensure your networking—like VPC peering or Private Link—is secured before moving sensitive production data. Using Terraform for workspace deployment will keep your environments consistent and auditable during the transition.
Are you planning on using the Databricks Migration Assistant tool, or are you doing the conversion manually to ensure custom libraries and dependencies are handled correctly for each specific cluster?
Focus heavily on the "Medallion Architecture" (Bronze, Silver, Gold). Organizing your data layers clearly in Databricks makes governance and data discovery much easier for the end-users.
Absolutely agree, Christopher. Implementing the Medallion structure early was the key to our project's success, especially when explaining data lineage to our stakeholders.
Hi Linda, we actually found that for complex jobs with many legacy JAR dependencies, the manual route was safer. We used the Migration Assistant for the simple SQL-based ETLs, but for the heavy lifting, we rebuilt the environment using Repos (now Folders) to integrate directly with our existing GitLab CI/CD pipelines. It helped us maintain version control throughout the whole move.