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ETL testing and validation services

Data is omnipresent today, right from the daily supermarket experience to stationing a satellite accurately in its orbit. Have you ever thought about the interesting background processes that crunch, process, discard, transform, and slice and dice the data that is presented to you in a desired format? Have you ever wondered about the quantum of the data in the world? Where and how the information pertaining to this humongous data is stored? Here is the answer – Extract-Transform-Load (ETL).

Infosys ETL Testing solution provides a unified platform for tools and artifacts on data processing, loading, transformation, and reporting that improves efficiency in process, tools, and technologies. We offer diverse services and differentiators, and our goal is to achieve progressive automation with the help of machine learning and a pool of over 2500 experts.

End-to-end ETL testing

Today, end-to-end ETL testing has become an integral part of every organization due to the lack of the following:

  • Comprehensive testing in enterprise data warehouse systems, which are complex and huge
  • Uniform regression criterion resulting in incomplete test coverage
  • End-to-end automation as it involves huge volumes of data and manual validation proves to be ineffective

Infosys Data Warehouse Testing solution helps you address these challenges while improving the effectiveness of data warehouse testing, data migration, compliance testing, test data management (TDM), and big data testing. The solution streamlines and accelerates the testing of data warehouse applications by offering a user-friendly, comprehensive, and integrated Web-based workbench. It is platform independent and can support multiple databases using built-in business logic to reduce manual errors. By providing extensive comparisons and reports, it assists in testing data migration procedures that enable identification of defects easily.

Infosys Data Warehouse Testing solution features

  • Data Comparison component  helps  identify anomalies in transformed and migrated data
  • Data Quality Analyzer addresses the concerns related to data quality. It performs data quality analysis through metadata, statistical, relationship, pattern, and business rules-based analysis
  • Business intelligence (BI) report verifier component helps in independent validation of reports
  • Big data validation feature   offers   querying interface for big data stores, data comparison between relational database management system (RDBMS) vs Hadoop Distributed File System (HDFS) and statistical analysis for unstructured data
  • Test data management feature includes data ingestion and synthetic data generation

Master data management (MDM) validation

Master data management is a comprehensive method for enabling an enterprise to link all its critical data to one file called ‘master file’ that provides a common point of reference. It streamlines data sharing among personnel and departments when done effectively. Also, it can facilitate computing in multiple system architectures, platforms, and applications.


Data quality analysis

Data quality analysis refers to validating the reliability and effectiveness of data, particularly in a data warehouse. It helps check the quality of data, perform data matching, identify mismatches, and report anomalies.


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