We successfully migrated a clientโs MySQL databases to DB using a dual-approach that maintained 100% data integrity while enabling real-time analytics.
After struggling with batch-based updates and analytics delays, we implemented:
- One-time historical data load via JDBC for the complete dataset
- Continuous Data Capture (CDC) with Confluent Kafka for real-time updates
- Delta Live Tables (DLT) pipelines for automated data transformation
The biggest challenge? Some tables lacked primary keys, making CDC difficult. We developed custom logic to handle these edge cases while working with dev teams to implement proper keys.
RESULTS: Not just data migration, but a complete transformation:
- Real-time data availability (vs. previous 3-hour lag)
- 99.7% pipeline reliability with automated error handling
- Zero impact on production MySQL performance
Most surprising insight: The complex decimal precision issues (MySQL's Decimal(40,16) vs. Spark's max Decimal(38,16)) required creative solutions, not just technical workarounds.
What data migration challenges are slowing YOUR business down? Let's connect if you're facing similar hurdles.
