Data Pipeline
An automated series of data processing steps that moves and transforms data from sources to destinations.
In Depth
A data pipeline is a set of automated processes that move data from one or more sources through a series of transformation steps to a destination system. Pipelines handle the full data lifecycle: ingestion (collecting data from sources), transformation (cleaning, enriching, aggregating), validation (checking data quality and completeness), loading (writing to the destination), and monitoring (tracking pipeline health and data freshness). Pipelines can be batch-oriented (processing data in scheduled intervals), streaming (processing data in real-time), or hybrid (micro-batch). Modern data pipeline tools include Apache Airflow, Dagster, Prefect, dbt, Fivetran, and custom solutions built on message queues and stream processors.
How AI for Database Helps
AI for Database connects to databases at any point in your data pipeline, whether source systems, staging areas, or final warehouses.
Related Terms
ETL
Extract, Transform, Load—a data integration process that moves data from source systems into a data warehouse.
Data Warehouse
A centralized repository optimized for analytical queries across large volumes of historical data from multiple sources.
Workflow
A sequence of automated steps triggered by an event or schedule that processes data and takes actions.
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