A data pipeline is a sequence of processes that move data from one system to another while transforming, validating, or enriching it along the way. Pipelines often extract data from source systems, process it through intermediate steps, and load it into destinations such as a database, data warehouse, or analytics tool. They may run in real time or as batch processing jobs. Modern pipelines rely on queues, schedulers, and distributed workers to ensure reliable data flow across systems.
Why it matters
Data pipelines automate the movement of information across an organization, ensuring that reports, dashboards, and downstream systems have accurate and up-to-date data. A well-designed pipeline is resilient to failures, scalable as data grows, and observable so operators can detect issues early. Faulty pipelines can create inconsistencies, break analytics, or disrupt automated decision-making.
Examples
ETL pipelines that pull data from APIs, transform it into structured form, and load it into a warehouse are common examples. Lessons such as Simple Data Pipelines and ETL and ELT Pipelines explore these workflows.