Best Test Data Management Tools for IBM Db2

IRI RowGen generates rows ... billions of rows of safe, intelligent test data in database, flat-file, and formatted report targets ... using metadata, not data. RowGen synthesizes and populates accurate, relational test data with the same characteristics of production data. RowGen uses the metadata you already have (or create on the fly) to randomly generate structurally and referentially correct test data, and/or randomly select data from real sets. RowGen lets you customize data formats, volumes, ranges, distributions, and other properties on the fly or with re-usable rules that support major goals like application testing and subsetting. RowGen uses the IRI CoSort engine to deliver the fastest generation, transformation, and bulk-load movement of big test data on the market. RowGen was designed by data modeling, integration, and processing experts to save time and energy in the creation of perfect, compliant test sets in production and custom formats. With RowGen, you can produce and provision safe, smart, synthetic test data for: DevOps, DB, DV, and DW prototypes, demonstrations, application stress-testing, and benchmarking -- all without needing production data.

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.

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.

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.