Data Quality Validator (DQV)Kumaran Systems
|
Dignadigna GmbH
|
|||||
Related Products
|
||||||
About
DQV is Kumaran Systems' data quality and testing platform for teams who move, mask, or validate large volumes of data. It compares source and target datasets field by field, flags drift, and generates a mismatch report instead of manual spreadsheet checks.
It covers five areas: field-level comparison with drift detection, migration mapping between schemas, deterministic PII masking, record- and table-level validation with on-the-fly correction, and synthetic data generation for teams without production data to test against.
It connects to SQL Server, Oracle, MySQL, PostgreSQL, AWS, Azure, GCP, flat files, JSON, XML, and REST APIs, and plugs into Informatica, Databricks, and CI/CD pipelines, or runs standalone as a library or CLI tool.
In production, DQV has validated 26.6 million bank records in under 22 minutes. A free trial is available, alongside individual, enterprise, and on-premises licensing.
|
About
digna is a data quality and observability platform designed to monitor, analyze, and validate data directly within enterprise data environments.
It combines anomaly detection, time-series analytics, and validation into a unified system that helps teams detect issues early and understand how data behaves over time.
Core Capabilities
* Data Anomaly Detection
Identifies changes in data volume, distribution, and behavior using statistical methods and AI-driven models without relying on manually defined rules.
* Time-Series Analytics
Built-in analytical methods (regression, pattern detection, seasonality analysis) allow users to interpret trends and deviations directly within the platform.
* Data Timeliness Monitoring
Tracks expected data arrival times and identifies delays across pipelines and data flows.
* Data Validation
Supports rule-based validation with reusable templates and centralized definitions of allowed values.
* Schema Change Tracking
Detects structural changes in dat
|
|||||
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
|||||
Audience
ETL testers, DBAs, developers, functional QA and API testers at enterprises running data migrations, masking PII, or building compliance-grade data pipelines — especially in banking, insurance, telecom, and the public sector.
|
Audience
Data warehouses, data lakes, data lakehouses, banks, retail, hospitals
|
|||||
Support
Phone Support
24/7 Live Support
Online
|
Support
Phone Support
24/7 Live Support
Online
|
|||||
API
Offers API
|
API
Offers API
|
|||||
Screenshots and VideosNo images available
|
Screenshots and Videos |
|||||
Pricing
No information available.
Free Version
Free Trial
|
Pricing
No information available.
Free Version
Free Trial
|
|||||
Reviews/
|
Reviews/
|
|||||
Training
Documentation
Webinars
Live Online
In Person
|
Training
Documentation
Webinars
Live Online
In Person
|
|||||
Company InformationKumaran Systems
Founded: 1992
United States
kumaran.com/products/dqv/
|
Company Informationdigna GmbH
Founded: 2019
Austria
www.digna.ai
|
|||||
Alternatives |
Alternatives |
|||||
|
|
|
|||||
Categories |
Categories |
|||||
Data Quality Features
Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management
|
||||||
Integrations
IBM Netezza Performance Server
MariaDB
MySQL
PostgreSQL Maestro
SAP HANA
SQL Server
Snowflake
|
Integrations
IBM Netezza Performance Server
MariaDB
MySQL
PostgreSQL Maestro
SAP HANA
SQL Server
Snowflake
|
|||||
|
|
|