data-pipeline

To build a reliable ETL data pipeline with Airflow and dbt, you should design DAGs for orchestration and use dbt for transformations, ensuring idempotent data loads and robust data quality checks.

The best way to implement data quality checks in an ELT pipeline is to integrate validation steps directly into your dbt transformations and Airflow DAGs to monitor data integrity and ensure reliable processing.

Apache Spark works with Airflow for scalable batch processing by using Airflow DAGs to schedule and trigger Spark jobs, enabling reliable and observable data ingestion from transactional databases to your warehouse.

You need Apache Airflow to orchestrate incremental data loads because it provides the DAG management required to schedule daily ingestion processes, ensuring data integrity and efficient processing.

Yes, you can use this approach for both batch and streaming data architectures, as the guidance covers designing scalable pipelines that support reliable and observable data workflows across both processing paradigms.

Your dbt transformation may fail during daily data ingestion if idempotency is not enforced, meaning previous pipeline runs interfere with current loads and compromise data quality and integrity.