This video compares two approaches to Change Data Capture (CDC), a data engineering pattern used to capture and track changes in a database table.

Databricks Delta Live Tables (DLT):

Requires manual configuration of the pipeline for pooling and updating table snapshots from the source database.
Involves writing Python code to define the source table, target table, and how to apply snapshots.
Can take several hours to fully configure, especially for complex pipelines.

DataForge:

Automates the entire CDC process, from data ingestion to building the target table with SCD (Slowly Changing Dimension) Type 1 and 2 applied.
Takes less than a minute to configure a CDC pipeline.
Easier to manage configurations with YAML files similar to Databricks Asset Bundles.
Offers additional features like data transformation rules and support for complex data types and streaming.
Overall

Both DLT and DataForge simplify CDC compared to manual methods.
DataForge offers a more automated and user-friendly solution, especially for managing multiple CDC pipelines.

00:00 Introduction
00:53 Delta Live Table CDC Blog
10:05 DataForge Source Setup
14:58 DataForge Databricks Integration
16:10 Automated CDC Demo
18:42 SCD Type 1 and 2 in DataForge
21:53 Exporting DataForge Configs to YAML
23:24 Summary