Data Pipeline

Data pipelines automate the flow of data through your organization. Components include: sources (databases, APIs, events), ingestion (batch or streaming), processing (transformation, enrichment), storage (warehouses, lakes), and consumption (dashboards, ML models). Types: batch pipelines (scheduled, process accumulated data) and streaming pipelines (real-time, process events as they occur). Orchestration tools like Airflow, Prefect, or Dagster manage pipeline execution, dependencies, and monitoring. Key concerns: reliability (handling failures), scalability (growing data volumes), observability (tracking data quality and lineage), and maintainability (clear code, documentation). Well-designed pipelines are crucial for data-driven organizations.