XDM is a comprehensive test data platform supporting various database systems. It streamlines processes for Oracle, Db2, VSAM, PostgreSQL, and SQL Server databases as well as Cloud platforms like Google BigQuery and Snowflake, facilitating identification, extraction and masking of data between different instances utilizing specific database utilities.
XDM test data platform supports:
XDM streamlines the test data supply process for Oracle systems. Identify, extract, mask, and apply your data between different Oracle database instances.
Using database utilities
XDM uses Oracle Data Pump for transporting one-to-one copies quickly. It uses SQL*Loader to quickly load complete tables.
Unified data stream
XDM handles all the Oracle data types and brings them to a unified form during the process so that data can easily be compared and connected with data from other platforms. Consistent masking between Oracle databases and other databases is possible as well.
Connectivity
XDM is compatible with Oracle instances installed on servers as well as cloud-compatible, pluggable databases.
XDM streamlines the test data supply process for Db2 systems. Identify, extract, mask, and apply your data between different Db2 database instances.
Using database utilities
XDM uses the database utilities for transporting one-to-one copies quickly. On z/OS it uses UNLOAD/LOAD. The load implementation supports the internal and external format. It is also possible to transfer LOBs and XML data. For LUW, the import and export utility is used.
Unified data stream
XDM knows about all the DB2 data types and brings them into a unified form inside the process, so data can easily be compared and connected with data from other platforms. It also handles encoding transformation between ASCII, Unicode, and EBCDIC. Consistent masking between Db2 databases and other database platforms is possible.
Connectivity
XDM supports Db2 on z/OS, LUW, and i/series.
XDM makes your data in VSAM and sequential datasets testable. Identify, extract, mask, and apply your data between different VSAM datasets.
Accessing the data
XDM ships with the file bridge server that is installed on the z/OS host to make the data accessible. Virtual views are defined on top of the datasets. The views can be created using Cobol copybook descriptions.
Using database utilities
Bulk network operations and the usage of the DFSORT utility improves the performance while sending and accessing the data.
Unified data stream
Character data, numbers, and packed decimals are mapped to typical database types. The data can easily be compared and connected with data from other platforms. Consistent masking between VSAM data and other database platforms is possible.
Cross-copy to relational databases
Using the virtual views, DDL for tables in other database systems can be generated and the data copied over to a different platform.
XDM streamlines the test data supply process for PostgreSQL systems. Identify, extract, mask, and apply your data between different PostgreSQL database instances.
Using database utilities
XDM uses the copy utility for transporting one-to-one copies quickly.
Unified data stream
XDM works with all the PostgreSQL data types and brings them into a unified form. Inside the process, that data can easily be compared and connected with other database platforms. Consistent masking between PostgreSQL databases and other database platforms is possible as well.
XDM streamlines the test data supply process for SQL Server systems. Identify, extract, mask, and apply your data between different SQL Server database instances.
Using database utilities
XDM uses the BCP utility for transporting one-to-one copies quickly. For fast loading of complete tables, it is also able to build BCP data files from scratch.
Unified data stream
XDM knows about all the SQL Server data types and brings them into unified form inside the process so that data can easily be compared and connected with data from other database platforms. Consistent masking with SQL Server databases and other database platforms is possible as well.
Connectivity
XDM is compatible with SQL Server instances installed on Windows or Linux platforms. Also, cloud Azure DB instances can be connected.
XDM streamlines the test data supply process for IMS systems. Identify, extract, mask, and apply your data between different IMS database instances.
Accessing the data
XDM uses IMS Connect to access the data inside an IMS system. The hierarchical database is mapped to a relational database view.
Unified data stream
XDM knows about the hidden columns connecting the hierarchical segments that are added to the tables. The data is brought to a unified form inside the process so that data can easily be compared and connected with data from other platforms. Consistent masking between IMS databases and other databases is possible as well.
XDM streamlines the test data supply process for Bigquery systems. Identify, extract, mask, and apply your data between different BigQuery database instances.
Using database utilities
XDM uses the BigQuery Streaming API for transporting one-to-one copies quickly.
Unified data stream
XDM knows about all the BigQuery data types and brings them into a unified form inside the process so that data can easily be compared and connected with data from other database platforms. Consistent masking between BigQuery databases and other database platforms is possible as well.
Implementation scenario
Typically, a BigQuery data lake is used to collect all test data from various systems into one data pool, where the data is masked and can be made available to the desired test environment. The data is then copied back into the application’s original database platform, such as Db2, Oracle, SQL server, etc.
XDM streamlines the test data supply process for Snowflake systems. Identify, extract, mask and apply your data between different Snowflake database instances.
Using database utilities
XDM uses SQL Data Transport for transporting one-to-one copies quickly. The implementation of a batch mode ensures high performance.
Unified data stream
XDM knows about all the Snowflake data types and brings them into a unified form inside the process so that data can easily be compared and connected with data from other database platforms. Consistent masking between Snowflake databases and other database platforms is possible as well.