This repository tackles one of the trickier corners of modern data engineering: implementing Slowly Changing Dimensions (SCD Type 1 and Type 2) as streaming pipelines using Databricks' Lakeflow Declarative Pipelines (formerly Delta Live Tables, or DLT). It offers concrete examples in both Python and SQL, which is unusual — most SCD tutorials pick one flavor and stick with it.

For the uninitiated, SCD patterns are how data warehouses handle dimension records that change over time:

• Type 1 overwrites old values (you lose history, but the table stays small).
• Type 2 preserves history by adding new rows with validity timestamps (great for auditing, painful to implement correctly in streaming contexts).

What makes this repo interesting is that it bridges a real gap. Databricks recently rebranded DLT to Lakeflow and pushed streaming SCD support via APPLY CHANGES INTO, but public examples are still thin — most Google results return marketing pages or Databricks' own docs. Having a working reference implementation with side-by-side Python and SQL versions is genuinely useful for teams trying to migrate batch merge logic into declarative streaming pipelines.

Who would benefit:

• Data engineers currently wrangling MERGE statements in Databricks who want to move to declarative pipelines.
• Teams evaluating Lakeflow vs. rolling their own CDC handling on Delta Lake.
• Analytics engineers coming from dbt who want to understand how the same SCD patterns look in a streaming-first framework.

The zero-star status probably reflects newness and a narrow audience rather than quality — Databricks-specific tooling doesn't attract drive-by GitHub stars, but the people who need it, really need it.