data-engineering

To prevent train-serve skew in AI data pipelines, enforce a single-source-of-truth for feature transformations using dbt or Feature Views, ensuring identical logic across training and serving. This governance rule eliminates data drift and divergence.

SCD Type 2 modeling in dbt requires deterministic practices like is_current filtering to isolate active records. Applying these governance rules ensures accurate historical tracking and prevents accidental data overwrites in your warehouse.

Yes, this data engineering framework works with DuckDB and dlt for ELT ingestion. It enforces explicit incremental loading and cursor-based changes, providing governed templates that standardize ingestion logic across your data pipelines.

Data quality contracts in Airflow or Dagster are enforced by integrating tools like Great Expectations and Soda. This framework standardizes quality checks across orchestration platforms to prevent invalid records from reaching serving layers.

Yes, you can use RRF-based hybrid retrieval for RAG context lakes. This framework governs vector and streaming retrieval patterns, ensuring deterministic retrieval practices across real-time inference architectures.

To enforce tenant isolation in ELT data pipelines, apply pre-filtering rules before transformation. This governance practice ensures deterministic data segregation, preventing cross-tenant data leakage during dbt processing.