XDM
BigQuery
BigQuery is a central platform for modern analytics and data products, but test data is often still managed with ad-hoc SQL and brittle scripts. XDM integrates BigQuery into your enterprise-wide test data strategy, enabling cloning, subsetting and masking across cloud and on-prem systems. Teams work with business-oriented models instead of raw tables, while XDM automates secure, consistent provisioning of BigQuery test data.
Unified test data across BigQuery and relational systems
XDM’s entity and domain modeling lets you describe business objects such as customers, contracts or orders once and then link their technical representation in BigQuery and other databases. Relations between entities can span multiple systems, so data slices for a specific test can be assembled consistently across cloud and on-prem sources. Row-level processing uses these relations at runtime to determine all dependent records and move exactly the data you need, no more and no less. This unified model makes BigQuery a first-class citizen in end-to-end integration, regression and performance testing.
Unified test data across BigQuery and relational systems
Consistent masking and synthetic data for BigQuery
With XDM’s modification engine, sensitive data is anonymized or pseudonymized as it flows between BigQuery and other systems, using the same central rules everywhere. Deterministic masking ensures that identical input values are transformed into consistent outputs across tables, projects and even different platforms, preserving referential integrity while remaining GDPR-compliant. Existing BigQuery tables can act as lookup sources for realistic values, and the same mechanisms can be used to generate synthetic test data at scale. This enables you to run realistic analytics and ML tests in BigQuery without exposing production identities or violating internal compliance policies.
Consistent masking and synthetic data for BigQuery
Automation, workflows and integration into your toolchain
XDM workflows orchestrate all steps around BigQuery test data: validation of input parameters, data extraction and loading, masking, clean-up of previous runs, and notifications. These workflows can be triggered via the public REST API from CI/CD pipelines, schedulers or test automation frameworks, so BigQuery test data provisioning becomes just another automated step in your delivery process. Hooks enable custom pre- and post-processing, for example to invalidate caches, trigger downstream jobs or call external services once data is available. All executions are logged and reported centrally, giving you full transparency about which data was moved when and by which process.
Automation, workflows and integration into your toolchain
Self-service test data ordering for BigQuery users
Through the Data Shop, even non-technical users can request BigQuery-based test data in their own business language, selecting products, customer groups or time periods instead of thinking in terms of datasets and partitions. Behind each order, an XDM workflow takes care of all technical details, including cross-system selections, masking and imports into the desired BigQuery project or test dataset. Approval steps, execution windows and scheduling can be configured so that heavy BigQuery jobs run at appropriate times without impacting production workloads. This combination of self-service, governance and automation lets organizations leverage BigQuery safely and efficiently as a central analytics backbone for testing and quality assurance.
Self-service test data ordering for BigQuery users
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Test Data Management
XDM is a complete test data management platform that enhances agile teamwork, automates test data workflows, and supports multiple databases with secure masking.
Mask sensitive data
Secure sensitive data with XDM’s Masking Tool. Protect privacy and ensure compliance with advanced data masking for all environments.