ETL
- A three-step pipeline that gathers data from multiple sources, standardizes it, and stores it in a data warehouse for analysis.
- Enables consistent, query-ready datasets by cleaning, validating, and formatting incoming data.
- Commonly used by organizations to consolidate disparate data sources for reporting and decision-making.
Definition
Section titled “Definition”ETL stands for Extract, Transform, and Load; it is a process used in data warehousing to collect, clean, and organize data from various sources for analysis and reporting.
Explanation
Section titled “Explanation”- Extract: Data is retrieved from different sources (for example, databases, spreadsheets, and text files). This phase involves identifying relevant data, accessing the sources, and transferring the data to a staging area or the data warehouse.
- Transform: Extracted data is cleaned, validated, and reformatted to ensure consistency and compatibility with the data warehouse. Tasks in this phase include data cleaning, transformation, validation, and integration.
- Load: The transformed data is written into the data warehouse. This phase includes loading the data into target tables and may involve indexing and partitioning to optimize query performance.
Examples
Section titled “Examples”Retailer example (phases)
Section titled “Retailer example (phases)”- Extract: A retailer extracts sales data from its point-of-sale system, customer data from its CRM system, and inventory data from its supply chain management system.
- Transform: The retailer combines sales and customer data into a single table, removes duplicate entries, and converts the data into a format suitable for analysis and querying.
- Load: The retailer loads the combined sales and customer data into a table in the data warehouse, indexes the data by customer ID and date, and partitions the data by month, quarter, and year.
Use cases
Section titled “Use cases”- Consolidating data from multiple systems so organizations can analyze it and make better data-driven decisions.
- Supporting reporting and analysis to help improve operations, increase revenues, and better serve customers.
Related terms
Section titled “Related terms”- Data warehousing
- Data cleaning
- Data transformation
- Data validation
- Data integration
- Data loading
- Data indexing
- Data partitioning
- Data warehouse