Best Test Data Management Tools for SQL Server

It can quickly generate data based on table names, column specifications, field sizes, data types, and other predefined constraints. These generators can easily be tailored to suit your specific needs. You can produce substantial amounts of data within just a few clicks in SQL Server Management Studio. The system allows for column-aware data generation, enabling the creation of data in one column that depends on the values in another. Users benefit from enhanced flexibility and manual oversight when crafting foreign key data. Custom generators that can be shared with your team are also available, allowing you to save regular expressions and SQL statement generators for collaborative use. Furthermore, you can write your own generators in Python, giving you the ability to create any additional data required. With seeded random data generation, you can ensure that the same dataset is produced consistently each time. Moreover, foreign key support helps maintain data consistency across various tables, making the process even more efficient and reliable. This versatility in data generation significantly streamlines workflows and enhances productivity for database management tasks.

Datanamic Data Generator serves as an impressive tool for developers, enabling them to swiftly fill databases with thousands of rows of relevant and syntactically accurate test data, which is essential for effective database testing. An empty database does little to ensure the proper functionality of your application, highlighting the need for appropriate test data. Crafting your own test data generators or scripts can be a tedious process, but Datanamic Data Generator simplifies this task significantly. This versatile tool is beneficial for DBAs, developers, and testers who require sample data to assess a database-driven application. By making the generation of database test data straightforward and efficient, it provides an invaluable resource. The tool scans your database, showcasing tables and columns along with their respective data generation configurations, and only a few straightforward entries are required to produce thorough and realistic test data. Moreover, Datanamic Data Generator offers the flexibility to create test data either from scratch or by utilizing existing data, making it even more adaptable to various testing needs. Ultimately, this tool not only saves time but also enhances the reliability of your application through comprehensive testing.

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.

The EMS Data Generator for MySQL is a remarkable application designed to create test data for MySQL database tables, offering options to save and modify scripts. This versatile utility enables users to replicate a production-like database environment, facilitating the simultaneous filling of multiple MySQL tables with test data. Users can specify which tables and columns to target for data generation, establish ranges of values, and create MySQL character fields based on specific patterns. Additionally, it allows for the input of custom value lists or the selection of values through SQL queries, along with tailored generation parameters for each type of field. With its diverse features, the tool simplifies the process of generating MySQL test data effectively. Furthermore, the Data Generator for MySQL includes a user-friendly console application, enabling one-click generation of test data using pre-defined templates. This added functionality streamlines workflows and enhances productivity for database developers.

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.

Tonic provides an automated solution for generating mock data that retains essential features of sensitive datasets, enabling developers, data scientists, and sales teams to operate efficiently while ensuring confidentiality. By simulating your production data, Tonic produces de-identified, realistic, and secure datasets suitable for testing environments. The data is crafted to reflect your actual production data, allowing you to convey the same narrative in your testing scenarios. With Tonic, you receive safe and practical data designed to emulate your real-world data at scale. This tool generates data that not only resembles your production data but also behaves like it, facilitating safe sharing among teams, organizations, and across borders. It includes features for identifying, obfuscating, and transforming personally identifiable information (PII) and protected health information (PHI). Tonic also ensures the proactive safeguarding of sensitive data through automatic scanning, real-time alerts, de-identification processes, and mathematical assurances of data privacy. Moreover, it offers advanced subsetting capabilities across various database types. In addition to this, Tonic streamlines collaboration, compliance, and data workflows, delivering a fully automated experience to enhance productivity. With such robust features, Tonic stands out as a comprehensive solution for data security and usability, making it indispensable for organizations dealing with sensitive information.

Envision extensive arrays of data entities, grasp their interconnections, and fine-tune associated data extractions to formulate ideal test datasets. Facilitate the comparison of files, even those located on different LPARs, thereby enhancing the capability to swiftly and routinely evaluate the repercussions of modifications. Streamline the intricate process of data management and test preparation by allowing developers and test engineers to execute data-related functions without the necessity of programming, scripting, SQL coding, or juggling multiple tools. Empower developers, test engineers, and analysts to achieve greater independence by allowing them to provision data as required, which lessens dependence on subject matter experts. Elevate application quality through improved testing scenarios, making the creation of comprehensive data extracts for testing more straightforward and enabling precise identification of the effects stemming from alterations in data components. By doing so, teams can respond more quickly to changes and enhance their overall productivity.

As interactions with customers increasingly transition from physical to digital environments, it becomes necessary to move beyond traditional face-to-face conversations. Instead, customers now convey their preferences and requirements through data. Gaining insights into customer behavior and validating our preconceptions about them also relies heavily on data-driven approaches. However, stringent privacy laws like GDPR and CCPA complicate this deep understanding even further. The MOSTLY AI synthetic data platform effectively addresses this widening gap in customer insights. This reliable and high-quality synthetic data generator supports businesses across a range of applications. Offering privacy-compliant data alternatives is merely the starting point of its capabilities. In terms of adaptability, MOSTLY AI's synthetic data platform outperforms any other synthetic data solution available. The platform's remarkable versatility and extensive use case applicability establish it as an essential AI tool and a transformative resource for software development and testing. Whether for AI training, enhancing explainability, mitigating bias, ensuring governance, or generating realistic test data with subsetting and referential integrity, MOSTLY AI serves a broad spectrum of needs. Ultimately, its comprehensive features empower organizations to navigate the complexities of customer data while maintaining compliance and protecting user privacy.

The rapid test data generation engine, equipped with approximately 70 integrated functions and an expression processor, allows users to create intricate test data that encompasses dependencies, internal structures, and relationships. This innovative product automatically examines existing database schemas and identifies the master-detail key relationships without requiring user intervention. Additionally, the Value Library offers a collection of predefined datasets that include names, countries, cities, streets, currencies, companies, industries, and departments. Features like Variables and Named Generators facilitate the sharing of data generation attributes across similar columns. Furthermore, the intelligent schema analyzer enhances the realism of your data without necessitating further modifications to the project, while the "data by example" capability streamlines the process of making data more lifelike with minimal effort. Overall, this tool stands out for its user-friendly approach in generating high-quality test data efficiently.

Syntho is generally implemented within our clients' secure environments to ensure that sensitive information remains within a trusted setting. With our ready-to-use connectors, you can establish connections to both source data and target environments effortlessly. We support integration with all major databases and file systems, offering more than 20 database connectors and over 5 file system connectors. You have the ability to specify your preferred method of data synthetization, whether it involves realistic masking or the generation of new values, along with the automated identification of sensitive data types. Once the data is protected, it can be utilized and shared safely, upholding compliance and privacy standards throughout its lifecycle, thus fostering a secure data handling culture.