Data Pipeline
A sequence of processes that collects, moves, validates, transforms, and delivers data from one or more sources to target systems.
Data pipelines make operational and analytical information available consistently for reporting, automation, personalization, and AI.
A pipeline ingests product events, validates schemas, removes duplicates, enriches records, loads a warehouse, and alerts owners when freshness or quality thresholds fail.
A successful job does not guarantee correct data. Pipelines can propagate source errors quickly, and unmanaged dependencies, schema changes, or reprocessing can create inconsistent results.
Pipelines may operate in batches or streams and include ingestion, storage, transformation, orchestration, quality checks, metadata capture, error handling, and delivery. Reliable designs address schemas, idempotency, retries, lineage, observability, security, and recovery.
Systems Architecture
Apache Airflow Documentation — What is Airflow? — https://airflow.apache.org/docs/apache-airflow/stable/index.html; IBM — What Is a Data Pipeline? — https://www.ibm.com/think/topics/data-pipeline