motherduck-build-data-pipeline

Design an end-to-end MotherDuck data pipeline by defining raw, staging, and analytics boundaries to ingest, curate, and serve data. The pipeline sequences transformations like deduplication and type normalization to produce analytics-ready serving tables or views.

Structure raw, staging, and analytics layers by staging source truth first, then applying deduplication and type normalization. Promote curated data through a transformation sequence to materialize final serving tables, ensuring analytics-ready outputs without losing original source fidelity.

You need DuckLake instead of native MotherDuck storage only when your pipeline truly demands open table formats. The pipeline defaults to native storage but selects DuckLake for specific Lakehouse publishing requirements when open format compatibility is necessary.

Add data validation checks to your ETL orchestration by applying recommended tests for counts, freshness, uniqueness, and business metrics. The pipeline design integrates these validation checks across the transformation sequence to ensure analytics-ready outputs.

You can publish analytics-ready serving views to a Lakehouse by evaluating your publishing requirements during pipeline design. The workflow coordinates materialization decisions and uses DuckLake specifically when open table format publishing is required.

The transformation sequence needed to turn raw data into analytics-ready outputs includes deduplication, type normalization, and staged promotion. It sequences transformations from landing through staging to final serving tables, applying validation checks throughout the modeling process.