What Is ETL (Extract, Transform, Load)?
ETL stands for Extract, Transform, Load — a three-phase process used to move data from one or more source systems, convert it into a suitable format, and load it into a target system such as a data warehouse, database, or analytics platform.
A data pipeline process that extracts data from sources, transforms it into a useful format, and loads it into a destination.
ETL is fundamental to data automation. The "Extract" phase pulls data from source systems (databases, APIs, files, applications). The "Transform" phase cleans, validates, formats, and enriches the data. The "Load" phase writes the processed data to the destination system.
Traditional ETL tools are designed for data engineers and require significant technical expertise. Modern automation platforms bring ETL capabilities to business users through visual data mapping tools and AI-powered data transformations.
In workflow automation, ETL concepts appear everywhere. When you sync contacts between HubSpot and Mailchimp, you're extracting contact data, transforming field names and formats, and loading the results. The automation platform handles this automatically.
Reverse ETL is an emerging pattern where processed data is pushed from a data warehouse back into operational tools. For example, sending enriched customer segments from Snowflake back into your CRM for personalized outreach.
Related Terms
Data Sync
The process of maintaining consistent data across multiple systems by automatically propagating changes.
Data Transformation
The process of converting data from one format, structure, or value to another within a workflow.
API (Application Programming Interface)
A set of protocols that allows different software applications to communicate and share data with each other.
More Data & Analytics Terms
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