data-pipeline

Build reliable data pipelines by separating orchestration from computation, enforcing idempotent steps, and instrumenting data quality checks. Use Airflow for orchestration, Spark for computation, and dbt for transformations to ensure reproducibility and auditability across batch and streaming workloads.

Handle backfills and CDC in ETL workflows by designing idempotent pipeline steps and implementing strategies for CDC-friendly pipelines. This approach ensures reproducibility and auditability, allowing reliable data replays without duplicating records or corrupting downstream datasets.

Add data quality checks and observability to an ELT pipeline by instrumenting validation steps throughout transformation and ingestion. This detects anomalies early, enables rapid remediation, and maintains reliable data pipelines across batch and streaming workloads using tools like dbt and Airflow.

Separating orchestration from computation in data pipelines optimizes resource usage and maintains clear responsibilities. Using Airflow for orchestration and Spark or dbt for computation ensures scalable, observable, and reliable batch and streaming ETL/ELT workflows.

Data pipelines fail during backfill or reprocessing when steps lack idempotency and robust error handling. Enforcing idempotent operations and implementing backfill strategies ensures reproducibility, prevents duplicate records, and maintains auditability across batch and streaming workloads.

Use dbt for data validation in streaming workloads by integrating it with CDC-friendly pipelines and orchestration frameworks. This instruments data quality checks and observability to detect anomalies early, enabling reliable transformations across batch and streaming data pipelines.