A data pipeline is a set of automated steps that collects data from one or more sources, transforms it into a usable format, and delivers it to a destination like a data warehouse, lake, database, or analytics tool. It ensures data moves reliably and on time so teams and AI systems can run reporting, automation, and machine learning using consistent, trusted data.
Key Components of a Data Pipeline
Most data pipelines include:
- Sources: Applications, databases, files, APIs, event streams, sensors, or third-party platforms.
- Ingestion: Batch loads (scheduled) or streaming (real time).
- Transformations: Cleaning, deduplication, normalization, joins, enrichment, and calculations.
- Destination: Data warehouse, data lake, lakehouse, operational store, or feature store.
- Orchestration: Scheduling, dependencies, retries, and workflow management.
- Observability: Monitoring freshness, volume, schema changes, failures, and data quality checks.
- Security and governance: Access controls, encryption, lineage, and audit logs.
Common Types of Data Pipelines
- Batch pipelines: Move data on a schedule (hourly, daily) for reporting and backfills.
- Streaming pipelines: Process events continuously for near-real-time analytics and alerts.
- ELT vs ETL:
- ETL transforms data before loading it into the destination.
- ELT loads raw data first, then transforms it inside the warehouse or lakehouse.
- Operational pipelines: Sync data between production systems for workflows and automation.
- ML pipelines: Prepare training data, build features, and support model scoring.
How Data Pipelines Work Today
Modern pipelines often run in cloud environments and use modular tools that can be versioned, tested, and deployed like software.
A typical workflow:
- Extract data from sources (connectors, CDC, logs, events).
- Load into a staging area or raw zone.
- Transform using SQL or code, often with reusable models.
- Validate with data quality tests and schema checks.
- Publish curated datasets for dashboards, apps, and AI use cases.
- Monitor for failures, delays, and drift, then alert and auto-retry.
Teams also manage data contracts and schema evolution so downstream reports and AI agents do not break when upstream data changes.
Frequently Asked Questions
What is the difference between a data pipeline and a data workflow?
A data pipeline focuses on moving and transforming data between systems. A workflow is broader and can include non-data tasks like approvals, notifications, or triggering business actions.
What is CDC in a data pipeline?
CDC, or change data capture, streams inserts, updates, and deletes from a database so downstream systems stay in sync with minimal delay.
Why do data pipelines fail?
Common causes include API rate limits, upstream outages, schema changes, bad data, permission issues, and misconfigured schedules or dependencies.
What is data pipeline orchestration?
Orchestration is the coordination of pipeline steps, including scheduling, dependency management, retries, and logging.
How do teams measure data pipeline health?
They track uptime, run success rate, data freshness, latency, row counts, error rates, and data quality test results.
