Compare the Top Test Data Management Tools that integrate with Chef as of November 2024

This a list of Test Data Management tools that integrate with Chef. Use the filters on the left to add additional filters for products that have integrations with Chef. View the products that work with Chef in the table below.

What are Test Data Management Tools for Chef?

Test data management tools enable IT professionals and developers to create non-production test data that simulates real company data in order to reliably test applications and systems with data that's similar to production data. Compare and read user reviews of the best Test Data Management tools for Chef currently available using the table below. This list is updated regularly.

  • 1
    Delphix

    Delphix

    Delphix

    Delphix is the industry leader in DataOps and provides an intelligent data platform that accelerates digital transformation for leading companies around the world. The Delphix DataOps Platform supports a broad spectrum of systems, from mainframes to Oracle databases, ERP applications, and Kubernetes containers. Delphix supports a comprehensive range of data operations to enable modern CI/CD workflows and automates data compliance for privacy regulations, including GDPR, CCPA, and the New York Privacy Act. In addition, Delphix helps companies sync data from private to public clouds, accelerating cloud migrations, customer experience transformation, and the adoption of disruptive AI technologies. Automate data for fast, quality software releases, cloud adoption, and legacy modernization. Source data from mainframe to cloud-native apps across SaaS, private, and public clouds.
  • 2
    GenRocket

    GenRocket

    GenRocket

    Enterprise synthetic test data solutions. In order to generate test data that accurately reflects the structure of your application or database, it must be easy to model and maintain each test data project as changes to the data model occur throughout the lifecycle of the application. Maintain referential integrity of parent/child/sibling relationships across the data domains within an application database or across multiple databases used by multiple applications. Ensure the consistency and integrity of synthetic data attributes across applications, data sources and targets. For example, a customer name must always match the same customer ID across multiple transactions simulated by real-time synthetic data generation. Customers want to quickly and accurately create their data model as a test data project. GenRocket offers 10 methods for data model setup. XTS, DDL, Scratchpad, Presets, XSD, CSV, YAML, JSON, Spark Schema, Salesforce.
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