4 YAML Files Instead of PySpark: How We Let Analysts Build Data Pipelines Without Engineers

A data engineering team replaced its complex, developer-dependent PySpark pipelines with a declarative system, enabling analysts to build data marts independently. The new stack uses `dlt` for data ingestion via YAML, `dbt` with `Trino` for SQL-based transformations, and `Airflow` for orchestration. This approach dramatically reduced pipeline delivery time from several weeks to a single day. By abstracting away Python and infrastructure complexities, analysts can now focus on business logic using familiar SQL and YAML configuration files.