Top 10 BEST Test Data Management Tools In 2024
Software test data management is the process of planning, designing, storing, and managing an application’s software or source codes for testing. The primary goal of test data management is to inspect and test the software’s quality. It manages the files, rules, and so on generated during the software testing life cycle.
It distinguishes between test and production data. It reduces and optimizes the size of software testing data as well as generates testing reports. A test data tool is used to implement the test data management process.
Any test data management tool will go through the following processing steps:
- In any system, data is stored in various formats, types, and locations. This data is subjected to a variety of rules. As a result, the test tool extracts appropriate test data from these data for the testing process.
- The tool extracts a subset of data from the selected test data gathered from multiple sources.
- Following the selection of the subset test data, the test tool masks sensitive test data, such as a client’s personal information.
- The tool now compares the actual data to the baseline test data to determine the application’s accuracy.
- The tool refreshes the test data to improve the application’s efficiency.
This article provides information on the fundamentals of test data management and the best tools for performing this task.
Read more: Why should Businesses Outsource Data Management Services
Top Test Data Management Tools
The Best Test Data Management Tools are listed below.
- K2View
- Avo iTDM
- DATPROF
- Informatica
- CA Test Data Manager (Datamaker)
- Compuware
- InfoSphere Optim
- LISA Solutions
- Delphix
- Solix EDMS
Let’s get started…!!
#1) K2View
K2View is the leading test data management (TDM) solution for complex environments. While preserving referential integrity, testers can quickly provision test data subsets on demand from any number and type of production source. Integration into DevOps CI/CD automation pipelines via API.
At rest or in transit, sensitive data (PII) is discovered and masked. In addition, the software provides synthetic test data generation, versioning, subset reservations, reporting, an authentication layer, and other features.
There are options for on-premises, cloud, and hybrid deployments.
#2) Avo iTDM – Intelligent Test Data Management
Avo’s intelligent Test Data Management (iTDM) allows you to generate reliable and relevant production-like test data with a few clicks. This synthetic data allows teams to expedite their entire testing process. The solution discovers and manages PII automatically (data discovery), secures sensitive data for PII compliance (data obfuscation), and provides data provisioning and generation.
It supports open architecture through the use of easily pluggable custom modules. It can handle billions of records on commodity hardware because it is built and deployed on open-source technologies and container frameworks.
You can use iTDM to:
- Accelerate application delivery by hastening to test.
- In non-production environments, identify non-compliant data.
- Comply with ever-changing, on-demand, and configurable data privacy regulations.
- Only relevant data should be generated and provided downstream.
#3) DATPROF – Simplified Test Data
DATPROF is an excellent tool for data masking, synthetic test data generation, Test Data Subsetting technologies, and a test data provisioning platform. There is no need for complex tools for test data management, according to DATPROF. All of our customers adore the ease of use of our software and the incredible technology that solves critical test data issues.
DATPROF also provides self-service portals through which teams can refresh their test environments or integrate test data provisioning within your CI/CD pipeline using the API.
#4) Informatica Test Data Management
Informatica Test Data Management Tool is excellent for automated data subsetting, data masking, data connectivity, and test data generation. It detects sensitive data locations automatically. This meets the increasing demand for test data.
It also meets all of the requirements of the application owner, infrastructure, developers, testers, and so on. Informatica provides a non-production dataset that meets the development team’s needs. It also includes integrated sensitive data discovery, which improves test data security.
#5) CA Test Data Manager (Datamaker)
CA Test Data Manager is another excellent tool for creating highly synthetic data. This tool’s design is very flexible to simplify testing functionality. It is a product of CA technologies and acquires the DataMaker of Grid-tools. CA Test Data Manager is called Agile Designer, DataFinder, Fast DataMaker, and DataMaker.
It offers high-performance data subsetting, data masking, test matching, and other features. The tool generates, stores, and reuses test data in a repository. We can access the data as needed using the tool’s on-demand service.
#6) Compuware
Another popular testing tool that provides optimized test data management is Compuware’s Test data tool. We can quickly generate test data using this tool. The tool can mask, translate, generate, age, analyze, and validate testing data. The tool’s new feature is that it meets all of the mainframe test conditions.
It supports all common file formats. It provides complete data privacy with all of these features. This data privacy influences the industry’s file and data management solutions and allows for efficient access to test data.
#7) InfoSphere Optim
The IBM InfoSphere Optim tool includes workflow and on-demand service capabilities. This feature is beneficial for continuous testing and agile software development. The tool performs real-time data testing, employs appropriately sized test databases, and optimizes and automates the test data management process.
The tool helps organizations reduce costs and accelerate application delivery by speeding up the application development process. It analyzes and provides updated test data to developers and testers on demand. These features provide a comprehensive testing solution and reduce risk during the testing or training.
#8) LISA Solutions
LISA Solutions is an automated testing tool that generates a virtual dataset with high functional accuracy. The tool can import test data from various sources, including excel sheets, XML, log files, etc. Testers or developers can easily manipulate and integrate the test data into a single location.
Automatic data masking safeguards sensitive information without violating any security policies. It also offers dynamic data stabilization, which validates test data based on business rules. Another tool feature is virtual test data self-healing, which increases the viability of virtual test data.
#9 Delphix
Delphix Test data tool provides high-quality and faster testing. Redundant data is shared throughout this process during development, testing, training, or reporting. This data sharing is known as data virtualization or virtual data. The tool’s virtual data provides complete, full-size, and real data sets in a matter of minutes and takes up very little space.
It also saves money on storage. The tool boosts productivity by delivering and configuring applications and databases automatically. The tool operates on public and private cloud infrastructure, which means it provides services and charges based on usage.
#10 EDMS Solix
Solix’s test data tool generates test data subsets automatically for testing, development, masking, patching, training, and outsourcing. In addition, the tool generates and manages clone production data subsets from large databases.
These clone data subsets are created using business rules defined by the organization, which reduces creation time and infrastructure costs by up to 70%. These correct and realistic data subsets produce more accurate results. The tool reduces the need for unnecessary security risks while also saving time and space.
Conclusion
The preceding article provides basic information and features of the best test data management tools. After examining all of these tools, we can conclude that each has unique features, but they all use the same test data process.
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