https://community.databricks.com/t5/community-articles/migrating-from-mysql-to-databricks-real-time-insights-that/m-p/112230#M376 <P><SPAN>We successfully migrated a client’s MySQL databases to DB using a dual-approach that maintained 100% data integrity while enabling real-time analytics.</SPAN></P><P><SPAN>After struggling with batch-based updates and analytics delays, we implemented:</SPAN></P><P><SPAN>- One-time historical data load via JDBC for the complete dataset</SPAN></P><P><SPAN>- Continuous Data Capture (CDC) with Confluent Kafka for real-time updates</SPAN></P><P><SPAN>- Delta Live Tables (DLT) pipelines for automated data transformation</SPAN></P><P><SPAN>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.</SPAN></P><P><STRONG>RESULTS: Not just data migration, but a complete transformation:</STRONG></P><P><SPAN>- Real-time data availability (vs. previous 3-hour lag)</SPAN></P><P><SPAN>- 99.7% pipeline reliability with automated error handling</SPAN></P><P><SPAN>- Zero impact on production MySQL performance</SPAN></P><P><SPAN>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.</SPAN></P><P><SPAN>What data migration challenges are slowing YOUR business down? Let's connect if you're facing similar hurdles.</SPAN></P> Tue, 11 Mar 2025 05:58:58 GMT yadvendra_ksh 2025-03-11T05:58:58Z https://community.databricks.com/t5/community-articles/migrating-from-mysql-to-databricks-real-time-insights-that/m-p/112230#M376 <P><SPAN>We successfully migrated a client’s MySQL databases to DB using a dual-approach that maintained 100% data integrity while enabling real-time analytics.</SPAN></P><P><SPAN>After struggling with batch-based updates and analytics delays, we implemented:</SPAN></P><P><SPAN>- One-time historical data load via JDBC for the complete dataset</SPAN></P><P><SPAN>- Continuous Data Capture (CDC) with Confluent Kafka for real-time updates</SPAN></P><P><SPAN>- Delta Live Tables (DLT) pipelines for automated data transformation</SPAN></P><P><SPAN>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.</SPAN></P><P><STRONG>RESULTS: Not just data migration, but a complete transformation:</STRONG></P><P><SPAN>- Real-time data availability (vs. previous 3-hour lag)</SPAN></P><P><SPAN>- 99.7% pipeline reliability with automated error handling</SPAN></P><P><SPAN>- Zero impact on production MySQL performance</SPAN></P><P><SPAN>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.</SPAN></P><P><SPAN>What data migration challenges are slowing YOUR business down? Let's connect if you're facing similar hurdles.</SPAN></P> Tue, 11 Mar 2025 05:58:58 GMT https://community.databricks.com/t5/community-articles/migrating-from-mysql-to-databricks-real-time-insights-that/m-p/112230#M376 yadvendra_ksh 2025-03-11T05:58:58Z