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Product Overview

Data Subset is a tool designed to create smaller, representative subsets of production data for use in testing, development, and training environments. It operates primarily within z/OS environments, often requiring specific subsystems depending on the data sources being accessed. The architecture typically involves configuration files that define data extraction rules, masking policies, and connection parameters.

These files specify which tables and columns to include in the subset, how to mask sensitive data, and how to connect to the source databases. Data Subset may expose APIs for integration with other testing and development tools, enabling automation of the data subsetting process. It often integrates with existing security systems to control access to the extracted data subsets, potentially using LDAP directories or other authentication providers.

Key components include data extraction modules, masking engines, and data delivery mechanisms. Alternatives include manual data extraction and competing products like IBM Infosphere Optim. Understanding the architectural components and integration points is crucial for effective deployment and operation.

Frequently Asked Questions

What does Data Subset do?

Data Subset is a tool designed to extract smaller, representative portions of production data for use in testing environments. This allows organizations to test applications with realistic data without exposing sensitive information or overwhelming test systems with the full production dataset.

Is this a system, application, or tool?

Data Subset is a specialized toolset focused on data extraction and manipulation for testing purposes. It is not a complete system, application, framework, or middleware, but rather a utility that supports those broader systems.

What types of organizations use this?

Organizations that require robust testing of applications against realistic data, particularly those dealing with large production databases, benefit from Data Subset. This includes companies in regulated industries like banking, insurance, and healthcare where data privacy and compliance are critical.

When should we consider Data Subset?

A company should consider Data Subset when the need arises to create smaller, manageable datasets for testing, development, or training purposes. This is especially relevant when dealing with sensitive data that needs to be masked or when full production datasets are too large to be efficiently used in non-production environments.

What are the alternatives to Data Subset?

Alternatives to Data Subset include manual data extraction and masking processes, other commercial data subsetting tools, and custom-built scripts. Competing products include IBM Infosphere Optim Test Data Management Solution and Delphix.

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