data-pipeline

An idempotent data pipeline ensures re-runs produce the same results without duplication. You can design Airflow DAGs that orchestrate dbt transformations by incorporating partitioning, incremental loads, and backfill strategies for reliable batch processing.

Data quality gates validate data integrity at various stages of an ETL pipeline. You implement them by embedding validation checks within Apache Airflow workflows to prevent bad data from progressing through transformations and loading into data marts.

Handling schema evolution in data pipelines requires designing transformations that adapt to changing data structures. Using Apache Spark and dbt allows you to manage schema changes gracefully during both batch and streaming ingestion processes.

Apache Airflow is an orchestrator and should not handle heavy computation directly. For production-ready data pipelines, Airflow should delegate intensive processing tasks to Apache Spark or dbt to avoid performance bottlenecks.

Yes, you can use Apache Spark and dbt together in an ELT pipeline. Spark handles large-scale data loading and initial processing, while dbt manages the subsequent data transformations and modeling to populate data marts.

Partitioning and backfill strategies are essential for incremental loads because they allow data pipelines to process only new or updated data. This approach optimizes computational resources and ensures historical data accuracy during workflow re-runs.