Licensing

This document provides an overview of the licensing options for XDM. To ensure maximum flexibility and scalability, the licensing model is modular and based on several key factors:

Environment and Usage:
- Number of Users: The total number of users accessing the software.
- Size of the Productive Environment: The productive data volume of the relevant production systems.
Supported Connectors: Licensed per connector technology.
License Packages: Functional scope of the software.
Optional Components: Additional features that can be licensed individually.

The following sections outline the available options. Detailed descriptions of each variant can be found below.

Definition of Productive Data Volume

The productive data volume is the sum of the actually used storage space ("used space") of all production databases on those DBMS platforms for which XDM connectors are to be licensed.

The productive data volume does not include indexes, archives, or technical operational/support data, such as transaction logs, redo logs, TEMP/UNDO areas, or backup/snapshot data.

For each relevant DBMS platform, the productive data volume is determined as of the reference date based on the storage or usage statistics provided by the respective DBMS, using the used space of the production databases.

Licensed Connectors

Support for specific connector technologies is licensed separately. The following connector types are available:

Generic JDBC Database: Connects to database systems via JDBC using generic database capabilities. This connector supports generic data types and standard JDBC-based access, but does not include database-specific SQL dialect handling or native vendor utilities.
Google BigQuery: Connects to Google BigQuery as a data source via JDBC and bulk data APIs. Suitable for integrating analytical cloud data platforms into XDM-driven test data processes.
IBM Db2 for i: Connects to IBM Db2 for i (formerly AS/400) via JDBC. Suitable for accessing Db2 for i tables and database structures through the platform-specific JDBC interface.
IBM Db2 for z/OS: Connects to IBM Db2 for z/OS via JDBC and native IBM database utilities. For native processing, XDM uses native utilities. For Db2 for z/OS, the documented supported DDL object types include Alias, Database, Constraint, Drop, Grant, Index, Sequence, Table, Tablespace, and View.
IBM Db2 LUW: Connects to IBM Db2 for Linux, Unix, and Windows via JDBC and native IBM utilities. For native export scenarios, XDM uses the data utilities . This connector is intended for efficient table-based data extraction and provisioning using Db2-specific capabilities.
IBM IMS: Connects to IBM IMS databases via JDBC over IMS Connect. Suitable for integrating IMS production data structures into XDM processes where JDBC-based access is available.
Kafka Topics: Connects to Apache Kafka topics for event-driven or stream-oriented integration scenarios. Suitable for consuming or providing structured data in distributed messaging environments.
Microsoft SQL Server / Azure SQL: Connects to Microsoft SQL Server and Azure SQL via JDBC and Microsoft bulk data utilities. Suitable for high-performance extraction and loading scenarios in Microsoft database environments, in addition to standard JDBC-based processing.
Oracle Database: Connects to Oracle databases via JDBC and Oracle-native data transfer utilities. For native processing, Oracle environments may require execution of Data Pump and SQL Loader jobs, which are explicitly referenced in the XDM documentation. This connector is intended for efficient table-based extraction, loading, and test data provisioning in Oracle environments.
PostgreSQL: Connects to PostgreSQL via JDBC and PostgreSQL-native data transfer utilities. Suitable for both standard JDBC-based access and high-performance database-specific data transfer scenarios.
SAP API Connector: Connects XDM to SAP data integration tools via API. Suitable for integrating SAP business objects and SAP-managed enterprise data into test data provisioning and orchestration processes.
Snowflake: Connects to Snowflake as a data source via JDBC and bulk data APIs. Suitable for cloud-based analytical data platforms and large-scale structured data access.
UBS Hainer FileBridge: Provides access to files in various formats as relational sources via JDBC. Supported formats include CSV as well as mainframe-oriented formats such as VSAM and sequential files. This connector allows file-based data sources to be handled similarly to database tables within XDM.
Webservices / REST APIs: Connects to external systems through web service or REST API interfaces. Suitable for integrating application-level business objects and service-based data provisioning processes.

Functional Licensing

XDM functional licensing consists of a base package and, where required, separately licensed add-ons.

License Packages

The license packages are designed to address different needs and use cases, allowing customers to select the package that best fits their requirements.

Essential: The base package with core features for basic use cases.
Standard: Includes all Essential features plus extended functionality for more advanced scenarios.
Premium: Full feature set including all modules, integrations, and enterprise-level capabilities.

Add-ons

Add-ons can be licensed independently in addition to the selected base package. They extend the functional scope of XDM for specific project or platform requirements.

The following add-ons are available:

Synthetic Data Generation: Enables the creation of realistic, production-independent test data.
Synthetic Data Generation with AI: Enhances data generation with AI support.
Cloud Support: Provides installation and deployment support for cloud-based environments (Also part of the Premium License Package).

In addition to the add-ons listed above, individual features from the Standard or Premium package can also be licensed separately. Licensing of such features must be individually agreed with the vendor. Some add-ons or individually licensed features may depend on other licensed functions or technical prerequisites.

Package Comparison

The following table shows which components are included in each base package:

Component

Essential

Standard

Premium

Table Copy

Row Level Processing

Masking

Structure Compare

Ice Box

PII Finder

Data Shop

Configuration as Code

Permission Management

Hooks

Workflows

REST API

Test Data Finder

Test Data Reservation

Cloud Support

Events

Maintenance Mode

Detailed Component Descriptions

The following sections provide detailed descriptions of the components:

Synthetic Data Generation: Generates realistic, production-independent test data based on a domain model. Reusable generators can be combined to cover complete business scenarios. Output formats include CSV, JSON, and SQL.
Synthetic Data Generation with AI: Extends data generation with AI support to create test data with minimal effort. Uses domain models and optional meta-information to guide the generation of complete entity instances.
Table Copy: Copies full tables based on name patterns. Supports both native (faster, DB-specific) and compatibility (standard SQL) modes. Optional row reduction rules can be applied.
Row Level Processing: Copies subsets of rows based on a start condition. Automatically follows data dependencies—even beyond foreign key constraints or across databases.
Masking: Modifies data during copying using built-in functions and lookup tables. Masking definitions are reusable across environments and tasks.
Structure Compare: Compares database structures and tracks schema changes. Can generate DDL for setting up empty environments in supported databases.
Ice Box: Backs up test data sets for repeated use, ensuring identical test conditions even if data is modified by tests.
PII Finder: Detects sensitive data like names and addresses. Applies data masking methods automatically during copy operations. Includes preconfigured rules and supports customization.
Data Shop: A self-service portal for test data provisioning. Developers and testers can order data; XDM handles orchestration and delivery across systems [1].
Configuration as Code: Defines the XDM setup in version-controlled, human-readable files. Integrates with Git for automated deployment of configuration changes. XDM supports ZIP-based export structures that are suitable for version control and configuration-as-code scenarios.
Permission Management: Controls access to configuration objects. Permissions can be assigned to users or groups. Supports centralized permission administration for users and roles, including default permissions and permission transfer concepts.
Hooks: Custom scripts triggered during copy processes. Used for data preparation or interaction with external systems.
Workflows: Automates complex multi-step processes beyond copying. Runs tasks in sequence and reuses intermediate results to prepare data for testing.
REST API: Provides full process and configuration control via REST. Supports scheduling, triggering, and importing/exporting configurations.
Test Data Finder: Finds test data that matches user-defined criteria, helping to cover edge cases and improve test quality.
Test Data Reservation: Prevents test data from being used in parallel by multiple testers. Ensures data integrity in shared environments.
Cloud Support: Includes installation on Kubernetes or OpenShift clusters and enables the capability to run each execution in a separate pod for better workload and resource management. Supports cloud-native storage APIs such as Azure, Google, and AWS for synchronizing data storage.
Events: Executes custom actions in response to system events. These events can be triggered by user actions or system changes, such as data updates, configuration changes, execution starts, completions, and other important operations. For example, this is useful for sending notifications or triggering external workflows.
Maintenance Mode: Temporarily disables XDM for updates or backups. Displays custom maintenance messages to users attempting to log in.