dbt model contracts: a promise your tables keep
What dbt model contracts are, how to configure them, and where they are worth the friction: a practical, example-driven guide.
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Data Engineering Notes
Airflow, dbt, BigQuery, GCP, and AI where it belongs: inside the pipeline. Everything here comes from projects I've actually built and shipped.
From 5.8% to 76.5%: building a real-data ELT pipeline on West African financial inclusion
World Bank API → Airflow → DuckDB → dbt. What building on live data (BOM quirks, missing countries and all) teaches you that synthetic datasets never will.
Read articleStateful dbt retries on Airflow: replay only what failed
How persisting dbt artifacts to GCS turns a full-pipeline retry into a surgical re-run of failed models on Kubernetes.
Read articleKilling DAG proliferation with a YAML-driven DAG factory
When every new pipeline is a copy-pasted Python file, your Airflow repo rots. Config-as-code with validation at import time fixes it, and a working implementation shows how.
Read articleAI inside the pipeline: a PSI drift gate that stops bad scores before anyone sees them
Data engineering and ML meet in production. How a nightly credit-scoring DAG uses the Population Stability Index as a quality gate, so a broken feature never reaches a lending decision.
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