The main aim of ETL regression testing is to verify that the testing process enables the same output for a given input before and after the change. In this testing type, the various components of ETL codes are integrated to ensure all components work well after integration. In this testing type, the small components of ETL code are tested in isolation to ensure it works properly. In this testing type, the data quality is checked by running various tests on Dates, Null-checks, precision also syntax tests like invalid characters, patterns, and case order. In this testing type, SQL queries are run to validate that the data is correctly transformed according to the given business rules. This ETL testing type is performed to match schema, data types, length, indexes, constraints, etc., between source and target systems. It also ensures data completeness by checking that the data gets added to the target without any loss/truncation. This testing verifies if the number of records loaded into the target database is the same or not. It validates the source and destination data types to ensure the data is the same. This special testing technique is a sub-component of data warehouse testing, and it ensures complete extraction, proper transformation, and adequate loading of data to the data warehouse.Īdherence to transformation rules and compliance with all validity checks is a key factor. ETL testing ensures that the data extracted from heterogeneous sources and loaded into the data warehouse is accurate. The Meaning:Ī process of data extraction wherein Business Intelligence (BI) tools are used to extract the data from multiple sources, transform it into a consistent data type and load the data into a data warehouse. The Extract, Transform, and Load (ETL) process is the primary process used to effectively load data from source systems to the data warehouse and ETL testing should be leveraged by businesses to ensure seamless data migration across sources. Further, as organizations develop, consolidate, and transform data to data warehouses, they should adopt the best practices and processes for loading and transforming data and ensure no data loss might affect them. ![]() Businesses should ensure that the data is in the correct format and should be accurately processed, transformed, and loaded into the data warehouse.
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