GenRocket’s Approach to Synthetic Data Generationby admin on Sep 29, 2021
The Test Data Digest is a summary of information, ideas, and announcements published to the GenRocket community during the quarter. We encourage you to share it with your peers and share your feedback with us.
Mounting Interest in synthetic data generation was initially triggered by a need for secure data with the variety and volume needed for comprehensive testing. Now that interest is accelerating with new applications in ETL, Big Data analytics, IoT, and machine learning.
When synthetic data is used for software testing, there are two very different approaches:
- A synthetic data replica is produced by scanning and profiling a production data source
- Synthetic data is designed and generated dynamically based on test case requirements
The first approach involves scanning and profiling a production database to create a statistically representative synthetic data replica. This results in a secure and private synthetic version of production data that can safely be used for data analytics and business intelligence use cases.
However, statistically representative synthetic data is not suitable for software testing and quality assurance. That’s because a synthetic replica has the same limitations as the original production database from which it was derived. If data patterns, permutations and variations are missing from the production database, they will also be missing from the synthetic replica.
With GenRocket, synthetic data is designed by configuring specialized data generators to meet the criteria of a given test case using a self-service platform. This allows testers to have exactly the data they need when they need it.
Flight School Reaches New Heights
Flight School, introduced in May of this year, is an interactive, self-guided multimedia learning environment for maximizing the benefits of your GenRocket deployment.
Since that time, we’ve been continuously improving this immersive educational environment with deeper content for an expanded audience.
Now anyone can learn how to use GenRocket, regardless of their role in the organization, or prior experience with synthetic data generation.
- Organization Admin – Performs administrative tasks within the GenRocket web platform.
- Data Architect – Imports or creates the Data Model within each GenRocket Project.
- Test Data Engineer (TDE) – Uses advanced GenRocket features for designing test data.
- Software Testers – Any user who will need to use GenRocket to generate test data.
In addition to persona-based learning pathways, Flight School has a Welcome page with introductory videos and an Orientation page for new users. We’ve also added a new Specialty Flight Plan to cover vertical solutions and advanced features. Our X12 EDI Solution Accelerator for healthcare applications is an example of a Specialty Flight Plan.
Check out the new and improved Flight School and let us know what you think. We’re always interested in new ideas for taking Flight School to the next level.
Maximizing the Security and ROI of Test Data Management
During the quarter, we published a new web page describing the GenRocket solution for secure test data provisioning and lifecycle management. This information will provide security officers and business stakeholders with a solid understanding of GenRocket’s secure hybrid cloud computing environment and how it delivers unmatched data privacy and security for GenRocket’s Test Data Automation (TDA) solution.
This innovative approach allows testers and developers to create test data designs in a secure AWS Virtual Private Cloud (VPC), download test data designs over a secure connection protected by Transport Layer Security (TLS), and generate real-time synthetic data based on those designs safely behind the corporate firewall in the customer’s on-premise facility.
GenRocket’s SaaS-based solution is more secure than the approach used by traditional Test Data Management (TDM) systems for copying test data directly from a production database. And from a financial perspective, the GenRocket approach provides a lower cost of ownership and delivers a far superior ROI.
We invite you to review our new security page to learn about this advanced architecture and its many security provisions. There is also a detailed security checklist that describes each security feature and the threat protection it provides.
Read the overview of GenRocket Security here:
Download the GenRocket Security Checklist here:
Top New Features and Enhancements
All GenRocket product enhancements are designed to make it easier for testers and developers to generate their own synthetic data using our flexible self-service platform. During the quarter, our engineering team added many new capabilities designed to do just that. Here is a list of new features and enhancements we added to the GenRocket platform during June, July, and August. A summary of September enhancements will be published next month.
Top New Features for June
- Added Support for Sequencing EDI Segments and Parent-Child Sequencing
- Dynamic File Name and Directory Added to JSONSegmentMergeReceiver
- New NumberToWordsGen Generator
- New GitHubReceiver
- New ImageTemplateReceiver
- Scratch Pad Update
Top New Features and Enhancements for July
- Selective Test Data Case (G-Case) Download
- XTS Wizard Form and Performance Enhancements
- Organization Attributes Enhancement
- G-Repository Enhancements (Proxy Support and Additional Commands)
- New EDITemplateReceiver
- H2InsertV2Receiver Enhancement
- ImageTemplateReceiver Enhancement
- NumberToWordsGen Generator Enhancement
Top New Features and Enhancements for August
- XSD Choice Option
- Domain Tree View for XSD
- Added Web APIs to Browse Old Version Jars
- ExponentialDistGen Generator
- MatchStringEvalCaseGen Generator
- Test Data Queries (G-Queries) Platform Report Bug Fixes