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Automated Change Data Capture (CDC)

2 posts · Jul 2026 – Aug 2026

What this trail covers

This post details advancements in Automated Change Data Capture (CDC) technology, focusing on how new SQL features in Databricks simplify lift-and-shift migrations to the Lakehouse. It likely describes enhancements to the CDC system's architecture, performance, or reliability to handle challenging scenarios such as high-volume data changes, diverse data sources, or stringent latency requirements, specifically by leveraging SQL capabilities to ease the migration process.

All Databricks trails
  1. Busting SQL Migration Myths: How New SQL Features Make Lift-and-Shift to Lakehouse EasierThis post introduces new SQL features that simplify lift-and-shift migrations to the Databricks Lakehouse by enhancing Automated Change Data Capture (CDC) capabilities. It details how these features address challenges in handling high-volume data changes and diverse data sources, making migrations easier.
  2. Taking AUTO CDC to the next level: Solving the hardest real-world use casesThis post takes AUTO CDC to the next level by solving the hardest real-world use cases. It introduces new SQL features that simplify lift-and-shift migrations to the Lakehouse, addressing challenges like high-volume data changes, diverse data sources, and stringent latency requirements.
  3. NBCUniversal’s Seamless Migration: Unlocking Scalable Analytics with DatabricksThis post details NBCUniversal's migration to Databricks, focusing on how they unlocked scalable analytics. It highlights the technical challenges and solutions involved in migrating their data platform, likely involving data ingestion, transformation, and processing pipelines. The emphasis is on achieving scalability and efficiency through Databricks, suggesting architectural changes and optimizations were implemented to handle large volumes of data and complex analytical workloads.