data-engineering

Structuring dbt models for production-ready data pipelines involves designing scalable dbt layering and documenting downstream schema expectations. You can enforce this by running project structure checks to validate transformations before deployment.

The best way to orchestrate Airflow DAGs is by using production-ready templates that implement retries, XCom usage, sensors, and notification hooks. This ensures resilient schedules and provides fallback patterns for pipeline automation.

Enforcing data quality gates in an ETL pipeline requires running QA gate scripts and integrating dbt tests before deployments. This validation automation ensures data transformations meet quality standards and align on SCD strategies.

Yes, you need python3 and jq installed in your environment to automate dbt validation checks. These dependencies are required to execute the QA gate scripting and parse the JSON outputs from your data warehouse transformations.

You should choose ELT over ETL for modern data warehouse architecture when you need scalable dbt layering and inline transformations. Comparing these trade-offs helps determine the optimal pipeline architecture for your downstream analytics.