Data Integration Testing: Ensuring Quality for ETL and Data Consolidation

Testing of software systems is always challenging, but testing data integration systems is especially difficult. Complex logic for consolidating data from disparate sources, data quality problems in source systems, “surprise” changes in source systems, and other factors combine to make data integration testing uniquely challenging. Although concepts of unit testing, stream testing, and system testing are still important, they alone are not adequate to the task of ensuring quality in data warehousing and data integration systems.

This course uses a combination of lecture, examples, and practice to teach effective testing techniques for data integration. From data profiling to stress and regression tests, you’ll learn about effective models that can be used to apply the most powerful testing techniques throughout the data integration lifecycle.

You Will Learn

Why data integration testing is particularly challenging
The data quality challenges that are inherent in data integration systems and projects
Several testing techniques and the circumstances where each is most effective
How to test data integration systems throughout the lifecycle from requirements to deployment
Methods for effective test planning and test execution

Geared To

ETL and data integration system developers; data integration systems designers and architects; testing and quality assurance specialists; data warehousing and data migration project managers

Technical resources.

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