Data Quality Validator

Built for Every Byte That Matters

A complete data processing platform for modern enterprises, engineered to ensure quality at every stage of migration, masking, validation and synthetic generation.

Synthetic Data

Production‑Grade Synthetic
Data

Know More

Core Capabilities

Five Capabilities, One Unified Platform

See each capability in action. Every module is engineered to handle enterprise-scale data with audit-ready accuracy.

Compare

Supports different data structures
Memory efficient for large data sets
Field level comparisons with drift detection
Classifies differences and highlights efficient decisions
Generates a quick summary and a comprehensive mismatch report

Compare

Supports different data structures
Memory efficient for large data sets
Field level comparisons with drift detection
Classifies differences and highlights efficient decisions
Generates a quick summary and a comprehensive mismatch report

Migrate

Enables data aggregation features
Offers advanced data filtering capabilities
Provides mapping of field names between source and target
Supports deterministic masking and calculated field creation

Migrate

Enables data aggregation features
Offers advanced data filtering capabilities
Provides mapping of field names between source and target
Supports deterministic masking and calculated field creation

Mask

Efficiently implements deterministic masking algorithms
Enables both in-place and in-flight masking
Supports repeatable masking processes
Offers multiple masking techniques, including redaction and replacement

Mask

Efficiently implements deterministic masking algorithms
Enables both in-place and in-flight masking
Supports repeatable masking processes
Offers multiple masking techniques, including redaction and replacement

Validate

Ensures comprehensive and high-performance data quality validation
Provides a systematic list of errors and validation at a record level
Field level, record level and table level validation capabilities
On the fly data correction during runs

Validate

Ensures comprehensive and high-performance data quality validation
Provides a systematic list of errors and validation at a record level
Field level, record level and table level validation capabilities
On the fly data correction during runs

Synthetic Data

Meaningful & valid data- Name, Address, SIN, SSN and more
Numerical data - sequential or random values within a given range
String data - sequential or random values selected from a predefined list

Synthetic Data

Meaningful & valid data- Name, Address, SIN, SSN and more
Numerical data - sequential or random values within a given range
String data - sequential or random values selected from a predefined list

Benefits

Unlock Powerful Business Value with DQV

Data integration and validation that optimizes data quality and meets your business goals.

Cost Reduction & ROI

80–90% lower TCO
No consultant dependency
Runs on standard hardware

Efficiency & Productivity

90%+ faster validation
Zero manual errors
100% dataset coverage
Parallel processing power

Risk & Quality

Zero data loss migrations
Detect issues early
Validate at every level

Compliance & Privacy

HIPAA / GDPR-ready
Secure data masking
Flexible privacy options

Digital Transformation

Faster time-to-market
Legacy-to-cloud ready
CI/CD integration

Scalability & Readiness

15× performance boost
Any database, any format
Flexible deployment
Future-proof architecture

User Application

The Right Fit for Every Team

Hover any role to see how DQV fits its day-to-day workflow.

ETL Tester

Build realistic test datasets that protect sensitive information
Catch missing data, validate incoming feeds and report quality
Reproduce the data flow and processes that generate target datasets
Compare source and target datasets side by side, faster
Generate synthetic data to test and tune your data pipeline

DBA

Developer

Functional QA

API Tester

ETL Tester

Build realistic test datasets that protect sensitive information
Catch missing data, validate incoming feeds and report quality
Reproduce the data flow and processes that generate target datasets
Compare source and target datasets side by side, faster
Generate synthetic data to test and tune your data pipeline

DBA

Mask data in-flight, in-place, or deterministically
Move data between environments with precision
Keep referential integrity intact during migrations and updates
Meet regulatory compliance with advanced masking techniques

Developer

Export and import data across formats without writing glue code
Validate and report on the quality of every incoming dataset
Pinpoint and patch missing or invalid records
Diff two datasets for fast unit testing
Spin up synthetic data when production-grade data isn't available

Functional QA

Generate test data from existing database records in minutes
Compare datasets, exported files or databases with precision
Continuously validate and monitor data quality

API Tester

Generate synthetic payloads on demand
Reproduce the data flow and process used by target services
Compare actual versus expected results with field-level accuracy
Keep analytics and reporting clean with validated data
Plug straight into CI/CD pipelines

Supported Data Sources

Adapts to Your Data, Wherever It Lives

Database

SQL Server, Oracle, MySQL, PostgreSQL

Files

CSV, Delimited, Fixed Length, Excel (.xls / .xlsx)

Cloud Databases

AWS, Azure, Google Cloud

Connectors

REST APIs, Message Queues

Data Format

JSON, XML, Parquet

Extensible

Custom connectors available

Pricing

Simple, transparent pricing

Pay only for the users who access DQV and the data volume you process. Kumaran Systems is ISO 27001 & SOC 2 Type II certified.

All prices in USD, excluding applicable taxes

Starter

Trial Plan

14-Day Free Trial

No Credit Card

Try it for free

Personal

Individual

$21/mo

per user · billed monthly

Buy

Business

Recommended

Team

$32/mo

per user · billed monthly

Buy

Enterprise

$450/mo

per 10 users · billed monthly

Buy

On-Prem

Subscription

Talk to us

Cost per server instance billed annually (per 50 users)

Perpetual

Talk to us

Cost per server instance one-time payment (per 50 users)

Compare Plans & Know more...

FAQs

Technical Questions Answered

Quick answers to the questions data teams ask most often about DQV. Don't see yours? Reach out and we'll get back to you.

01How do you ensure zero data loss during migration?

DQV runs pre and post migration validation, comparing source and target datasets at the field level. Discrepancies are highlighted and surfaced in detailed reports so nothing slips through.

02What if our source and target schemas are different?

Built-in field mapping, type conversion and aggregation handle schema differences automatically. You configure the mapping once and DQV applies it on every run.

03How does DQV help with data quality assessment?

Validation runs at field, record and dataset levels. Quality reports show pass/fail counts, drift indicators and detailed mismatches so teams know exactly where to act.

04Can DQV handle PII data masking for compliance?

Yes — deterministic masking covers redaction, replacement, randomization and encryption. Recent benchmark: 99M records masked in under 20 minutes.

05How do you maintain data consistency across environments?

The Mask Info Store keeps masked values consistent across dev, test, staging and prod, so the same source row produces the same masked value everywhere.

06How can DQV accelerate our ETL testing?

Automated validation replaces hand-rolled spot checks. One client cut their full ETL validation cycle from 6 hours to 15 minutes.

07Can DQV handle large volumes of data?

Yes — parallel processing has handled 86M+ records in under 21 minutes in production workloads.

08How does DQV integrate with existing ETL tools?

DQV plugs into Informatica, Databricks and CI/CD pipelines. Run it as a library, a standalone CLI step, or a web app — pick the integration that fits your stack.

Alliance Partnerships

Partnerships that Create Lasting Value

Case Studies

Real Results, Proven Impact

1.24M RECORDS VALIDATED PER MINUTE

Validating Complex CreditLens Migrations for a Top Bank - 26.6M Records Checked in 21.5 Minutes

The bank's Credit Risk Technology's CreditLens Migration Testing business needed to migrate data from CreditLens into a new in-house Financial Assessment application across roughly 9 main transaction tables...

1.24MRECORDS VALIDATED PER MINUTE

Built for Every Byte That Matters

Compare

Migrate

Mask

Validate

Synthetic Data

Five Capabilities, One Unified Platform

Compare

Compare

Migrate

Migrate

Mask

Mask

Validate

Validate

Synthetic Data

Synthetic Data

Unlock Powerful Business Value with DQV

Cost Reduction & ROI

Cost Reduction & ROI

Efficiency & Productivity

Efficiency & Productivity

Risk & Quality

Risk & Quality

Compliance & Privacy

Compliance & Privacy

Digital Transformation

Digital Transformation

Scalability & Readiness

Scalability & Readiness

The Right Fit for Every Team

ETL Tester

ETL Tester

DBA

Developer

Functional QA

API Tester

Adapts to Your Data, Wherever It Lives

Database

Files

Cloud Databases

Connectors

Data Format

Extensible

Simple, transparent pricing

Trial Plan

Individual

Team

Enterprise

Subscription

Perpetual

Technical Questions Answered

Partnerships that Create Lasting Value

Real Results, Proven Impact

Validating Complex CreditLens Migrations for a Top Bank - 26.6M Records Checked in 21.5 Minutes

How We Secured a 3.3M-Record Dev/UAT Migration for a Leading Bank - Masked, Encrypted & Validated in 14 Minutes

How We Automated ETL Regression Testing at Scale for an Enterprise Bank - 200M Records Validated in Under 2 Hours

23 Million Records, Zero Errors - How a Leading Canadian Bank Moved Off DB2 with DQV

96% Faster Data Reconciliation - Zero-Guesswork Validation for one of the Top 5 Canadian Bank's Global Derivatives Business

Informix to Oracle Migration for a Leading US Bank

US Automobile Giant Improves Mainframe Batch SLA from 32% to 99%

Automobile Manufacturer Migrates Warranty System from PowerBuilder to .NET in 13 Months

B2B Data Exchange Solution - 900 Dealers Synchronised via MS SQL Server

Canadian Furniture Retailer Migrates Mainframe to Web-First Technologies

Canadian Government Ministry Modernises Legacy Finance Audit System to Java Web Platform

College Modernises Student Information System - Every Business Rule Preserved

Data Visualisation & BI for Largest Retailer in Canada

Energy Company Migrates Critical Risk Assessment with 0 Downtime

Secure Internet-Based Authorization Document Entry System for Global Trade Standards

Global Retailer Modernises ERP - 318 Oracle Forms Objects Migrated to Web

Healthcare Company Rearchitects Secure HL7-Compliant PHR System

Manufacturing Systems Integration with JBoss Fuse & Apache Camel

Oracle Forms Migrated to Thin-Client Java - 80% Faster Conversion for Ontario Schools

Major Public Sector Corporation Migrates Oracle Forms to .NET - 30% Cost Savings

Public Sector Agency Modernises PowerBuilder to Oracle - Cost-Effective Semi-Automated Migration

Telecom Company Modernises Oracle Forms to J2EE - 1,200 Users Benchmarked

Leading US Bank Card Provider Migrates from Informix to Oracle - 100% Migration Achieved

Major US Transport Authority Revamps Capital Funding to J2EE - 10x Processing Volume

Monolithic to Microservices for a Leading Retail Bank in Canada

US Healthcare Agency Modernises Oracle Forms to J2EE - 30% Cost Savings