Definition: A data pipeline is an automated series of steps that collects data from one or more sources, transforms and validates it, and delivers it to a destination such as a data warehouse, an application or an AI model.
Pipelines extract data from databases, APIs, files or event streams, transform it by cleaning, joining and reshaping, and load it into a target system. In ETL the transformation happens before loading; in ELT raw data is loaded first and transformed in the warehouse. Pipelines run in batches on a schedule or continuously as streams, managed by orchestration tools such as Airflow or Dagster.
Reports, analytics and AI are only as reliable as the data feeding them. Pipelines with tests, monitoring and clear ownership prevent silent errors and stale data. For AI, they keep training sets, embeddings and retrieval indexes in sync with source systems.
Common uses include loading a warehouse such as Snowflake or Databricks, syncing operational systems, feeding RAG indexes and preparing features for machine learning.
Ryz data engineers design and run pipelines in your cloud, working on your team.
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