Migrating to Databricks offers a powerful, scalable platform that can revolutionize data processing and analytics. However, without thorough planning, unforeseen challenges can delay timelines, escalate costs, and reduce the potential benefits of this transition. Drawing on our extensive experience from guiding hundreds of data platform migrations, we’ve identified 14 critical pitfalls that organizations commonly face during Databricks migrations. Here’s a look at the top three:
1. Inventory Documentation
A clear, detailed inventory of all jobs, tables, and data sources is an essential first step in Databricks migration, yet it’s often overlooked. Without it, projects risk scope creep as missing elements surface later, adding unplanned tasks and costs. By documenting all resources up front, you establish a strong foundation that keeps the migration on track.
2. Access Management Challenges
Each system has its unique access protocols, and transitioning these to Databricks can be complex. By defining role-based access and working closely with security teams early on, you can streamline access management, avoiding costly delays caused by last-minute access troubleshooting.
3. Application-Level Testing
Data migrations bring subtle differences that can affect reporting accuracy, often leading to issues that arise post-migration. Conducting comprehensive application-level testing early on helps ensure data integrity and maintains stakeholder trust by catching discrepancies before they become costly errors.
The above represent only a fraction of what a successful migration entails. Download our complete guide to uncover the remaining 11 essential guidelines, covering everything from cost management to devOps strategies, timezone alignment, and data quality checks. Armed with these insights, you’ll be well-prepared to navigate the full spectrum of migration complexities and ensure a smooth, efficient transition.
