Best Test Data Management Tools for SQL Server

Mask, generate, subset, virtualize, and automate your test data with the DATPROF Test Data Management Suite. Our solution helps managing Personally Identifiable Information and/or too large databases. Long waiting times for test data refreshes are a thing of the past.

It can generate generators instantly based on field length, table and column names, and other constraints. These generators can be customized to your needs. SQL Server Management Studio makes it easy to create large amounts of data in just a few clicks. Column-intelligent Data Generation - Generate data in one column based upon the data in another. Foreign key data can be created with greater flexibility and manual control. Shareable custom generators – Save regexp and SQL statement templates to share with your colleagues. You can write your own Python generators to create any additional data you require. Seeded random data generation makes it possible to generate the same set of data each time. Foreign key support for generating consistent data across multiple tables.

Datanamic Data Generator allows developers to quickly populate databases with thousands upon rows of meaningful, syntactically correct data for database testing purposes. A blank database is useless for testing your application. Test data is essential. It is difficult to create your own test data generators and scripts. Datanamic Data Generator can help. This tool is available for developers, DBAs, and testers who require sample data to test a database-driven app. Datanamic Data Generator makes it easy to generate database test data. It will read your database and display tables and columns according to their data generation settings. To generate complete (realistic) test data, only a few entries are required. This tool can be used to create test data from scratch, or from existing data.

Delphix is the industry leader for DataOps. It provides an intelligent data platform that accelerates digital change for leading companies around world. The Delphix DataOps Platform supports many systems, including mainframes, Oracle databases, ERP apps, and Kubernetes container. Delphix supports a wide range of data operations that enable modern CI/CD workflows. It also automates data compliance with privacy regulations such as GDPR, CCPA and the New York Privacy Act. Delphix also helps companies to sync data between private and public clouds, accelerating cloud migrations and customer experience transformations, as well as the adoption of disruptive AI technologies.

EMS Data Generator for MySQL is a powerful tool that generates test data for MySQL database tables. It also allows you to save and modify scripts. This utility allows you to simulate a database production environment. It allows you to populate multiple MySQL database tables simultaneously, set value ranges and generate MySQL char fields using mask. You can also define tables and fields for data generation, create lists of values manually, select them from SQL queries, and set generation parameters for each type. There are many other features that allow you to generate MySQL test data quickly and easily. Data Generator for MySQL also includes a console application that allows you to quickly generate MySQL test data by using generation templates.

We can no longer rely upon real-life conversations as physical customer interactions shift to digital. Customers communicate their intentions and share their needs using data. Data is a key tool for understanding customers and testing our assumptions. Privacy regulations like GDPR and CCPA make deep understanding more difficult. This gap in customer understanding is bridged by the MOSTLY AI synthetic dataset platform. Businesses can benefit from a reliable, high-quality generator of synthetic data in many different applications. The story doesn't end there. MOSTLY AI's synthetic dataset platform is more versatile than any other synthetic data generator. MOSTLY AI's versatility makes it an indispensable tool for software development and testing. From AI training to explainability and bias mitigation, governance to realistic test data, with subsetting, referential integrity.

Tonic automatically creates mock datasets that preserve key characteristics of secure data sets so that data scientists, developers, and salespeople can work efficiently without revealing their identities. Tonic creates safe, de-identified data from your production data. Tonic models your production data from your production data to help tell a similar story in your testing environments. Safe and useful data that is scaled to match your real-world data. Safely share data across businesses, teams, and borders to create data that is identical to your production data. PII/PHI identification and obfuscation. Protect your sensitive data by proactive protection with automatic scanning, alerts and de-identification. Advanced subsetting across diverse database types. Fully automated collaboration, compliance, and data workflows.

With over 70 functions and an expression processor, the product allows users to quickly create test data using dependencies, internal structure and relationships. The product automatically analyzes an existing database schema and resolves master detail key structure (relationships). Value Library is a predefined data set that includes names, countries and cities, currencies, companies, sectors, departments, regions, and more. Named Generators and Variables feature allow you to share data generation properties with similar columns. Intelligent schema analyzer makes your data more realistic without any additional project modifications. Data by example makes data more realistic and less time-consuming.

Visualize large numbers of data objects, understand their relationships and tune related data extracts for optimal test data. Comparing files on different LPARs allows you to quickly and easily assess the impact of your changes. Developers and testers can simplify the complex task of managing and preparing data for testing. This allows them to perform data-related tasks without having to write programs or scripts, code SQL or use multiple utilities. Developers, analysts, and test engineers can be more self-sufficient by enabling them to provision data as needed, decreasing reliance on subject matter specialists. Better testing scenarios can improve application quality, making it easier to create data extracts for testing purposes and accurately identifying the effects of data changes.