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"ETL is short for extract, transform, load — three database functions that are combined into one tool to pull data out of one database and place it into another database.
- Extract is the process of reading data from a database. In this stage, the data is collected, often from multiple and different types of sources.
- Transform is the process of converting the extracted data from its previous form into the form it needs to be in so that it can be placed into another database. Transformation occurs by using rules or lookup tables or by combining the data with other data.
- Load is the process of writing the data into the target database." — Webopedia
"A properly designed ETL system extracts data from the source systems, enforces data quality and consistency standards, conforms data so that separate sources can be used together, and finally delivers data in a presentation-ready format so that application developers can build applications and end users can make decisions" (Wikipedia).
"ETL can be contrasted with ELT (Extract, Load, Transform), which transfers raw data from a source server to a data warehouse on a target server and then prepares the information for downstream uses" (TechTarget).
More from the data glossary
A definitive guide to data definitions and trends, from the team at Stitch.
- Analytics
- Big data
- Business intelligence (BI)
- Common table expression
- Data analytics
- Data architecture
- Data engineering
- Data enrichment
- Data exploration
- Data ingestion
- Data integration
- Data lake
- Data migration
- Data mining
- Data modeling
- Data pipeline
- Data preparation
- Data science
- Data visualization
- Data warehouse
- Data Wrangling: Definition and Examples
- Database
- Deduplication
- ELT
- ESB - What is an Enterprise Service Bus?
- ETL
- ETL pipeline
- Information lifecycle management (ILM)
- Machine learning
- Master data management (MDM)
- SaaS
- Service-oriented architecture (SOA)
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