TDspora EPAM Test Data Management tool
TDspora
Solution Overview
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Customer problem
An outstanding issue of quality test data generation in the light of various privacy regulations and the lack of representative, statistically correct, and lightweight datasets became the main problems of Test Data Management.
EPAM Solution
We suggest a solution that wraps up the complexities of test data management into an easy-to-use UI, keeping technicalities under control.
The customers may extract subsets of related entities from relational and non-relational storage, train ML models with controlled privacy budgets, and generate synthetic data for application development and QA teams.
1/6
Benefits
Easy Start
Start using the toll in less than two hours and no formal training.
Test Data Quality
Keep test data volume under control, and make it accurate, variable, and safe.
Low TTM
Lift data access restrictions, remove test data availability bottleneck.
Synthetic Data Generation
Generate enough test data from production or non-production samples.
Privacy Regulations Compliance
Quantifiable privacy level compliant with GDPR, CCPA, HIPAA, PCI DSS
Public Cloud Integration
Pre-built containers for AWS SageMaker and Google Cloud VertexAI
Features
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Privacy-preserving data generation: TDspora learns your data patterns and generates production-like data compliant with privacy regulations, eliminates the need for manual data classification and masking, and protects your data with a Differential Privacy approach
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Data migration: This enables distributed, fault-tolerant data migration between different types of data stores, allowing you to adjust the shape of the target data set, and helping to enrich test data
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Data sub-setting: Test Data management feature that allows you to traverse through data sources, following complex relationships and applying filters to minimize the footprint of test data while keeping it consistent
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Data sources: TDspora can be extended to support a wide range of SQL dialects and versions of databases. Built-in SQL translation engine enables quick adoption of new data sources following the customer demand
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Relationships management: TDspora extracts and visualizes data relationships as flexible, easy-to-understand graphs
Pricing Plans
Team
$9,600/mon
- Professional service hours - 80
- Minimum commitment - 6 months
- Retain statistical relationships
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Line of Business
$16,000/mon
- Professional service hours - 160
- Minimum commitment - 6 months
- Retain statistical relationships
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Enterprise
$25,000/mon
- Professional service hours - 320
- Minimum commitment - 3 months
- Retain statistical relationships
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Pricing Plans Comparison
| features | Open SourceFree | Team$9,600/mon | Line of Business$16,000/mon | Enterprise$25,000/mon |
|---|---|---|---|---|
| Professional service hours |  | 80 | 160 | 320 |
| Minimum commitment | | 6 months | 6 months | 3 months |
| Retain statistical relationships |  | | | |
| Accuracy reports | | | | |
| Accelerated training of models | | | | |
| Improved quality of synthetic data | | | | |
| Improved data protection with differential privacy | | | | |
| Retain semantical relationships | | | | |
| Privacy reports | | | | |
| Integrations with leading Cloud providers | | | | |
| Priority support | | | | |
Questions & Answers
How does TDspora handle dependencies across multiple databases?
Posted on July 18, 2022 by Ron
As of now, TDspora does not support cross-database relationships. However, the internal architecture of the accelerator makes the implementation of the feature relatively simple. Please the Contact Us form.
Posted on July 19, 2022 by SolutionsHub Support
How do I add or remove relationships not defined in the database?
Posted on April 17, 2022 by Daniel
TDspora supports manually defined relations as a simple script in the format
([,])-->([,])
For example,
ORDER(ORDER_ID)-->CUSTOMER(CUSTOMER_ORDER_ID)
Posted on April 20, 2022 by SolutionsHub Support
What are the incoming and outgoing depths?
Posted on February 24, 2022 by Ben
The second step of the pipeline configuration allows you to select a base table, the entry point of the subsetting algorithm, and two parameters “incoming” and “outgoing” depth. They regulate how far or deep from the base table the subsetting algorithm can go to include related tables. The “incoming depths” parameter enables the parent-to-child relationships and the “outgoing depths” – child-to-parent from the base table standpoint.
Posted on February 24, 2022 by SolutionsHub Support
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type
license type
categories
Integrates with
Apache Spark
Oracle – RDBMS
PostgreSQL
Google Cloud Platform: Vertex AI
Amazon Web Services: SageMaker
Tech Requirements
- Docker Community or Enterprise Editions 20.10+
Version
Updated on June 20, 2022
Documentation
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