Populate non-production environments with test data

case study

DevOps test data generation and provisioning

Challenge

A leading fintech provider in the UK lacked the necessary data to develop new product offerings at the speed demanded by its largest customers; which led to low developer productivity & inefficient spend in product development.

More specifically, 150+ software engineers, DevOps and DBAs were spending between 1-2 days to manually populate test environments with test data. Test data was created using "hacky" masking and seeding scripts which led to poor test case coverage & software pitfalls.

Solution

Agile test data generation and provisioning that brings DevOps into Dev & QA operations - API-driven engine easily integrated into a CI/CD pipeline.

Impact

Increased developer productivity & speed-to-market
Lowered development costs
Data compliance by design
Increased test coverage

Complex data schemas and referential integrity

Preserve primary and foreign key relationships and configure non-explicit relationships. Correlate logical relationships across tables

Ensure DDL including constraints, procedures, views, sequences among others is preserved in the test database

Generate test data in one-place with the same source and target database

Low-code YAML configuration

Configure parameters on a database level such as the "size_ratio" of the test database

Mimic business rules and logic for specific generated tables or columns

Generation data for only selected columns in a table and use existing data for other columns.

Read docs

Test data coverage

Automatically extract functional requirements from software applications

Measure test data coverage of your test cases

Cover all functional requirements and eliminate data redundancy

Applying modern data engineering practices to Dev & QA operations

Developer productivity

Applicaction development teams eliminate 1-2 days from development cycles with fast test data generation and provisioning.

Lowered development cost

Realized cost savings on test data acquisition and data provisioning.

Increased test coverage

Optimal coverage of test data for functional & non-functional requirements & detection of bugs before they reach production.

Automated compliance

No sensitive production data in non-production environments without compromising the development process.