A practical guide to building an internal data platform — combining dbt, Airflow, data warehouses, data contracts, and data quality checks into a coherent system your engineers and analysts actually want to use.
Dbt
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Building Internal Data Platforms: The Modern Data Stack in Practice -
dbt Core in Practice: Data Modeling That Doesn't Rot A complete hands-on guide to dbt Core — project structure, sources and staging layers, testing data quality, documentation, incremental models, and running dbt in production with Airflow.
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Data Pipeline Patterns: ETL vs ELT, Streaming, Batch Processing, and Orchestration A practical guide to data pipeline architecture — ETL vs ELT trade-offs, streaming with Kafka and Flink, batch transformation with dbt, and orchestration with Airflow and Dagster.