What is Data Pipeline?
A data pipeline is a series of automated steps that extract data from source systems, transform it for analysis, and load it into a destination (data warehouse, data lake, or analytics tool).
A data pipeline is a series of automated steps that extract data from source systems, transform it for analysis, and load it into a destination (data warehouse, data lake, or analytics tool). Also known as ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform).
Common pipeline tools: dbt (transformation), Fivetran/Airbyte (extraction), Apache Airflow (orchestration), and Dagster (modern orchestration).
Pipeline reliability is critical: a broken pipeline means stale data, which means wrong decisions. Production pipelines need monitoring, alerting, data quality checks, and automated recovery.
Data pipeline debt is a lesser-known form of technical debt. Poorly maintained pipelines accumulate: undocumented transformations, hardcoded business logic, orphaned tables, and performance bottlenecks that slow down analytics.
Why It Matters
Data pipelines are the plumbing of data-driven organizations. Unreliable pipelines lead to stale data, wrong metrics, and bad decisions. Pipeline quality directly determines analytics quality.
Frequently Asked Questions
What is a data pipeline?
Automated steps that extract data from sources, transform it, and load it into a destination for analysis. The backbone of data-driven decision-making.
What is the difference between ETL and ELT?
ETL transforms data before loading (traditional). ELT loads raw data first and transforms in the warehouse (modern). ELT is preferred because warehouses are powerful enough to handle transformations.
Related Terms
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