We are currently planning a massive migration from an on-premise Hadoop cluster to a Lakehouse architecture using Databricks and Delta Lake. What are the biggest pitfalls to avoid regarding data governance and cost optimization? We want to ensure we don't just "lift and shift" our existing technical debt into the cloud environment.
3 answers
Migration is the perfect time to audit your data. Don't move everything; use a "medallion architecture" (Bronze, Silver, Gold) to organize your new Lakehouse. The biggest pitfall is ignoring storage costs. While S3 or ADLS is cheap, compute costs on Databricks can spiral if you don't use Auto-Scaling and Spot Instances. Also, prioritize Unity Catalog for governance early on. It provides a unified layer for access control and lineage that Hadoop's Ranger often struggled to maintain across different components. Clean your logic during the move to save thousands in the long run.
In your migration strategy, are you planning to rewrite your MapReduce jobs into PySpark, or are you looking for a more automated transpilation tool to handle the legacy code?
Prioritize your metadata migration. Using a tool like Apache Atlas or Unity Catalog ensures that you don't lose the context of your data during the transition to the cloud.
Spot on, Laura. Metadata is the "glue" of the Lakehouse. Without it, your new cloud environment will quickly turn into a data swamp just like the old Hadoop cluster did.
We actually decided against automated tools, Steven. We found that legacy MapReduce code often contained logic that was no longer relevant. By manually rewriting critical paths into PySpark, we optimized our jobs to take full advantage of Delta Lake's Z-Ordering and caching. It was more work upfront, but the performance gains in our daily ETL runs were nearly 60% better than the old Hadoop jobs.