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Tuesday, May 20, 2014

Data Warehouse Testing Checklist

Unit testing checklist
 A checklist will aid database programmers to systematically test their code before formal QA testing.

  • Check the mapping of fields that support data staging and in data marts.
  • Check for duplication of values generated using sequence generators.
  • Check the correctness of surrogate keys that uniquely identify rows of data.
  • Check for data-type constraints of the fields present in staging and core levels.
  • Check the data loading status and error messages after ETLs (extracts, transformations, loads).
  • Look for string columns that are incorrectly left or right trimmed.
  • Make sure all tables and specified fields were loaded from source to staging.
  • Verify that not-null fields were populated.
  • Verify that no data truncation occurred in each field.
  • Make sure data types and formats are as specified during database design.
  • Make sure there are no duplicate records in target tables.
  • Make sure data transformations are correctly based on business rules.
  • Verify that numeric fields are populated precisely.
  • Make sure every ETL session completed with only planned exceptions.
  • Verify all data cleansing, transformation, and error and exception handling.
  • Verify stored procedure calculations and data mappings.

Integration testing checklist
An integration test checklist helps ensure that ETL workflows are executed as scheduled with correct dependencies.

  • Look for the successful execution of data-loading workflows.
  • Make sure target tables are correctly populated with all expected records, and none were rejected.
  • Verify all dependencies among data-load workflows—including source-to-staging, staging-to-operational data store (ODS), and staging-to-data marts—have been properly defined.
  • Check all ETL error and exception log messages for correctable issues.
  • Verify that data-load jobs start and end at predefined times.

Performance and scalability testing checklist
As the volume of data in a warehouse grows, ETL execution times can be expected to increase, and performance of queries often degrade. These changes can be mitigated by having a solid technical architecture and efficient ETL design. The aim of performance testing is to point out potential weaknesses in the ETL design, such as reading a file multiple times or creating unnecessary intermediate files. A performance and scalability testing checklist helps discover performance issues.

  • Load the database with peak expected production volumes to help ensure that the volume of data can be loaded by the ETL process within the agreed-on window.
  • Compare ETL loading times to loads performed with a smaller amount of data to anticipate scalability issues. Compare the ETL processing times component by component to pinpoint any areas of weakness.
  • Monitor the timing of the reject process, and consider how large volumes of rejected data will be handled.
  • Perform simple and multiple join queries to validate query performance on large database volumes. Work with business users to develop sample queries and acceptable performance criteria for each query.

System testing checklist
One of the objectives of data warehouse testing is to help ensure that the required business functions are implemented correctly. This phase includes data verification, which tests the quality of data populated into target tables. A system-testing checklist can help with this process.

  • Make sure the functionality of the system meets the business specifications.
  • Look for the count of records in source tables and compare them with counts in target tables, followed by analysis of rejected records.
  • Check for end-to-end integration of systems and connectivity of the infrastructure—for example, make sure hardware and network configurations are correct.
  • Check all transactions, database updates, and data-flow functions for accuracy.
  • Validate the functionality of the business reports.

Technical shakedown testing checklist
Because of the complexity of integrating various source data systems, you can expect some initial problems with the environments. A technical shakedown test is conducted before commencing system, stress and performance, and user acceptance testing to help ensure several needs are met.

  • Hardware is in place and has been configured correctly including ETL tool architecture, source system connectivity, and business objects.
  • All software has been migrated to the testing environments correctly.
  • All required connectivity between systems are in place.
  • End-to-end transactions—both online and batch transactions—have been executed and do not fall over.

 Ref. -  IBM datawarehouse

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