Check data threshold, alignment, and validate data flow. Apply transformation logic - Ensure data is transformed to match schema of target data warehouse.It is important to detect and reproduce any defects, report, fix the bug, resolve, and close bug report - before continuing to Step 5. Identify types of bugs or defects encountered during testing and make a report. Extract data from source systems - Execute ETL tests per business requirement.It is important to validate the mapping document as well, to ensure it contains all of the information. Design test cases - Design ETL mapping scenarios, create SQL scripts, and define transformational rules.If not done correctly, the aggregate report could be inaccurate or misleading. Make sure check keys are in place and remove duplicate data. Validate data sources - Perform a data count check and verify that the table and column data type meets specifications of the data model.It’s important to start here so the scope of the project is clearly defined, documented, and understood fully by testers. Identify business requirements - Design the data model, define business flow, and assess reporting needs based on client expectations.The process can be broken down into eight stages. Eight stages of the ETL testing processĮffective ETL testing detects problems with the source data early on-before it is loaded to the data repository - as well as inconsistencies or ambiguities in business rules intended to guide data transformation and integration. It differs from data reconciliation used in database testing in that ETL testing is applied to data warehouse systems and used to obtain relevant information for analytics and business intelligence. ETL testing refers to the process of validating, verifying, and qualifying data while preventing duplicate records and data loss.ĮTL testing ensures that the transfer of data from heterogeneous sources to the central data warehouse occurs with strict adherence to transformation rules and is in compliance with all validity checks. Data Wrangling: Speeding Up Data PreparationĮTL - Extract/Transform/Load - is a process that extracts data from source systems, transforms the information into a consistent data type, then loads the data into a single depository.Best Practices for Managing Data Quality: ETL vs ELT.Data Extraction Tools: Improving Data Warehouse Performance.What is Reverse ETL? Meaning and Use Cases.Stitch Fully-managed data pipeline for analytics.

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Etl processes