Purpose of XDM – A Technical Overview

At its core, XDM automates the provisioning of test data. It enables developers and testers to request and receive data for specific test cases using a centralized interface. The data can come from production sources (with masking), pre-existing test data, or be generated synthetically. XDM supports the end-to-end workflow — from data discovery and selection, through transformation and masking, to delivery in target environments.

The system allows users to define what data is needed in business terms (e.g., customer contracts, claims, invoices), and XDM translates these requirements into technical processes that extract, prepare, and deliver the data to the required environment.

The DataShop is the user-facing portal of XDM. It provides online forms for ordering test data, which are linked to predefined workflows in the backend. Users can select the target environment, specify business objects, and set additional parameters for the provisioning process. The ordering process can include optional approval steps and supports execution windows to avoid conflicts with productive processes.

From the user’s perspective, this enables a self-service mechanism: testers no longer need to manually request data from database administrators or write scripts. Behind the scenes, XDM executes the full workflow with all technical steps.

Finding suitable data for a test case is often one of the biggest challenges. The Test Data Finder helps users search across all test environments using indexed domain attributes. It builds a Test Data Index, allowing users to locate data that meets specific business or technical conditions (e.g., a contract with a specific status). The found data can then be directly transferred into a data provisioning order.

This is especially useful for edge cases or regression testing, where specific data constellations are needed to reproduce bugs or validate business logic.

Before test data can be delivered, it must be modeled and, in many cases, transformed:

XDM separates masking logic (how to mask) from structural knowledge (what to mask), enabling reuse across multiple environments and use cases.

When real data is not available or cannot be used due to compliance risks, XDM offers synthetic data generation. Based on a defined data model, XDM generates realistic, structured test data. This includes individual data generators for common fields (e.g., names, dates) and scenario generators that simulate entire business processes.

Synthetic data avoids GDPR issues and enables testing of edge cases that rarely occur in production data.

Test data provisioning is often more than just copying tables. XDM’s workflow engine allows chaining and automating multiple tasks: e.g., deleting old data, validating input, copying selected subsets, masking, notifying users, or triggering external systems.

XDM also supports Icebox, a component for freezing and restoring specific states of test data. This is useful for test repeatability: a known test case dataset can be saved and restored on demand, ensuring consistent test results.

In shared testing environments, concurrent tests must not interfere with each other. XDM provides a reservation mechanism that locks specific test data for exclusive use. During the reservation period, only the owner (or their group) can modify the data. After expiration, the data becomes available again. This avoids test conflicts and reduces the need for manual coordination.

XDM supports integration into existing IT landscapes through:

This makes XDM suitable for automated pipelines and test management workflows.

XDM uses the Configuration as Code approach. All configurations — environments, tasks, permissions — are stored in readable YAML files under version control (e.g., Git). This allows traceability, versioning, and easier collaboration across teams.

Changes made in the web interface can be exported and committed to version control. Conversely, changes in Git can be pulled into the system and reviewed before application.

XDM supports a wide range of relational databases (e.g., Oracle, Db2, PostgreSQL, SQL Server, Snowflake) and can move data across systems. It selects optimized transport methods (e.g., native utilities) depending on the platform. It handles foreign keys, table relationships, and structural differences automatically.

Additional components like structure comparison, row-level processing, and PII detection help maintain and adapt test data pipelines as application data structures evolve.

XDM includes a modern web-based UI for configuration, ordering, monitoring, and reporting. It provides centralized search, user-friendly forms, and dashboards (Grafana-based) for system monitoring.

Deployment is container-based (Docker), with support for Kubernetes and Helm charts, making it suitable for on-premise or cloud environments. XDM is designed to scale and adapt to enterprise-level requirements.

XDM is a modular, extensible, and automation-focused platform that supports the full lifecycle of test data provisioning. It reduces manual effort, improves test quality, and ensures compliance across environments. By combining self-service interfaces, flexible data