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ETL Testing > Learn ETL Testing > What is ETL (Extract Transform Load) Testing?
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What is ETL (Extract Transform Load) Testing?

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ETL (Extract, Transform, Load) testing is the process of verifying that data is accurately extracted from various sources, transformed into the required format, and loaded into a target system, such as a data warehouse or database. ETL testing involves: 1. *Extract testing*: Verifying data is correctly...
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ETL (Extract, Transform, Load) testing is the process of verifying that data is accurately extracted from various sources, transformed into the required format, and loaded into a target system, such as a data warehouse or database. ETL testing involves: 1. *Extract testing*: Verifying data is correctly extracted from sources. 2. *Transform testing*: Ensuring data is accurately transformed, including data type conversions, calculations, and aggregations. 3. *Load testing*: Validating data is correctly loaded into the target system. ETL testing aims to: 1. Ensure data accuracy and quality. 2. Validate data transformations and aggregations. 3. Verify data loading and integration. 4. Identify data quality issues and defects. 5. Certify ETL processes meet business requirements and data quality standards. ETL testing typically includes: 1. Data validation 2. Data verification 3. Data quality checks 4. Performance testing 5. Security testing By performing thorough ETL testing, organizations can ensure their data integration processes are reliable, efficient, and produce high-quality data for business insights and decision-making. read less
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Extract, transform, and load (ETL) is the process of combining data from multiple sources into a large, central repository called a data warehouse. ETL uses a set of business rules to clean and organize raw data and prepare it for storage, data analytics, and machine learning (ML).
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Extract, transform, and load (ETL) is the process of combining data from multiple sources into a large, central repository called a data warehouse. ETL uses a set of business rules to clean and organize raw data and prepare it for storage, data analytics, and machine learning (ML).

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