Model-based test automation for Big Data systems
Generate complete test suites for Solr and Kafka
Assemble data lookups in intuitive flowchart models, executing rigorous data-driven test suites in one click. Test Modeller and Test Data Automation generate consistent data journeys for Solr and Kafka on demand, unlocking rapid and rigorous Big Data testing.
More complex, more data intensive, more QA headaches?
Data streaming technologies like Apache Solr and Kafka allow systems to transform more data, from more sources, faster than ever before. However, this flexibility also adds QA complexity, risking bottlenecks that undermine the immense value that these technologies offer.
Testing Big Data systems quickly and rigorously requires on-demand data, drawn consistently from a range of sources. Manually finding, creating and provisioning cross-system data is simply too slow and unreliable. Misaligned data sets cause frustrating test failures, while tests consume one another’s data. Copied production data furthermore covers just a fraction of the combinations needed to test complex transforms, but does contain sensitive data. It therefore risks legislative non-compliance, while still allowing damaging bugs into production. Big Data testing instead requires comprehensive and compliant test data, available at the speed demanded by data-hungry test automation frameworks.
On-the-fly test data keeps pace with data streams
Test Modeller and Test Data Automation generate consistent data journeys on demand, executing rigorous Big Data tests at the click of a button. Using Test Modeller, testers and developers combine automated data lookups from a central catalogue in visual flowchart models, rapidly aligning parameters to produce consistent data journeys. This intuitive drag-and-drop data approaches produces comprehensive data for the most complex systems, applying automated test generation to produce consistent data from a range of sources.
During high-performance test execution, automated workflows wrap the rich data combinations in messages sent to Kafka Queues, replacing slow and complex data provisioning. Checkbox lists allow testers and developers to select one or more data-driven test quickly, tightly controlling the data fired off to Kafka and Solr applications. QA can control and measure Big Data testing precisely and accurately, testing vastly complex systems accurately in short iterations
Complete data journeys at the click of a button
This short video demonstrates rigorous data-driven test automation for a Solr Search Application, firing off SQL Server data via Kafka Queues. See how Test Modeller and Test Data Automation work in practice, discovering how:
Test Modeller visually combines data lookups from a shared test data catalogue, automatically passing variables from one lookup to the next to create consistent data journeys.
Flowchart modelling rapidly models a rich spread of data combinations, quickly generating complex data for rapid and rigorous automated testing.
Automated test generation resolves the linked-up data lookups “just in time”, generating data directly to databases or wrapping rich combinations in messages to send to Kafka queues.
A range of automated coverage techniques produce a complete spread of valid data journeys, rigorously testing Big Data systems before they are released to production.
Checkbox lists allow testers and developers to select one or more automated test, executing data-driven tests using consistently linked-up data journeys.
High-performance, automated workflows push the data to Kafka queues in a single click, replacing slow and unreliable data provisioning with on demand data hydration.
A Slack Bot provides updates as data is fed into Kafka queues, reporting reliably on the data that has been pushed to test and development environment