dbt Snapshots Can't Handle Late Data. So We Built One That Can

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dbt Snapshots Can't Handle Late Data. So We Built One That Can

Data backendanalytics Data Engineer Things

Mercedes-Benz released `dbt_iterative_snapshot` to handle late-arriving data in Databricks, fixing gaps left by standard dbt snapshots.

What: The open-source package automates the processing of historical batches chronologically, allowing dbt to repair historical records when new, late data arrives in the source system.

Why it matters: Standard snapshotting tools often struggle with out-of-order data, and this tool offers a programmatic fix for maintaining historical accuracy in data warehouses.

Takeaway: If you are using dbt on Databricks and struggle with late-arriving data, consider integrating `dbt_iterative_snapshot` to automate historical record repair.

Decoder

Late-arriving data: Data that arrives after its corresponding batch or processing window has closed, which can cause inaccuracies in time-series reporting.

Original article

Mercedes-Benz's open-source dbt_iterative_snapshot package for Databricks processes batches chronologically and automatically repairs historical records when late data arrives.

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