Databricks Migration Patterns: Strategies for Moving 50TB+ to Delta Lake

Migrating legacy data warehouses to modern cloud platforms like Databricks is a complex undertaking. In this post, I discuss the strategies I used to migrate over 50TB of data from an on-premise Hadoop cluster to Delta Lake on AWS.

The Challenge

Our legacy system was suffering from:

• High maintenance costs
• Lack of scalability during peak loads
• Inability to support ACID transactions

The Approach: Lift and Shift vs. Re-architecting

We chose a hybrid approach. For raw data ingestion, we used a “lift and shift” strategy to minimize disruption. However, for the consumption layer, we completely re-architected the ETL pipelines using Spark Structured Streaming and Delta Lake.

Key Learnings

1. Data Validation is Critical: We built a custom validation framework using PySpark to ensure 100% data parity.
2. Optimize for Write Throughput: Tuning the file sizes and using OPTIMIZE and Z-ORDER indexing significantly improved query performance.
3. Cost Management: Leveraging Spot Instances for non-critical batch jobs reduced our compute costs by 40%.

Conclusion

The migration not only modernized our stack but also empowered our data science teams to run ML workloads directly on the data lake, unlocking new insights.