Data Profiling Is Just The Start
TestGen profiles your tables, builds a data catalog, detects 27 common data hygiene issues, and generates thousands of data quality tests—automatically.
Open Source And Complete. Low Cost Enterprise Version
The Open Source Way To Data Profiling and Data Quality
What Is It? Full Featured AI-Driven Data Quality Open Source Software
51 Data Profiling Types
Uncover column-level insights and understand problematic rows.
27 Data Hygiene Detectors
After profiling is completed, TestGen automatically identifies 27 common data errors for your review.
Blazing-Fast In-Database Execution
TestGen pushes queries directly into your database for speed & security.
Data Catalog
A full 360° view of metadata, hygiene issues, PII risks, data test results, and Critical Data Elements.
Data Quality Scoring & Dashboards
Automated customizable scorecards with drill-down actions to improve data quality.
One Button Data Quality Checks
Instantly generate 1000s of automated data quality tests. Start fast, scale effortlessly.
Anomaly Detection
Stay ahead of data issues with automated alerts on freshness, volume, schema, and data drift.
Shareable Issue Reports
No time? Get influence and action on data quality with a single click.
All the Checkmarks. None of the Typical Cost Burden.
DataKitchen’s TestGen delivers enterprise-grade capabilities without enterprise-level costs, democratizing access to critical data quality tools.
What Are TestGen’s 51 Data Profiling Column Characteristics?
Data profiling is the periodic X-ray of tables in a database to gather extensive information about the contents of each column. Results are stored in a standard table in DataOps TestGen. This table is available for direct review and is used to derive downstream rules. Examples include: Averages, Column & Table Types & Names, Date Characteristics, Min/Max Value, Numeric Counts, Percentiles, Positions, Unique Values.
What Are Examples Of The Data Hygiene Issues TestGen Finds After Data Profiling?
Once data profiling is complete, Data Hygiene Detection Tests automatically confirm how closely data structures and assumptions match the actual contents of each column. Results can be used to assist the Data Engineer in refining data structure definitions and target the addition of data “patching” steps. Examples Include: Invalid Zip Code Format, Leading Spaces, Mostly Dates In String, Mostly not null/empty/filled values, Multiple Data Types Per Column Name, No Column Values Present, Non-standard Blank Values.
What Are The 31 Data Tests TestGen Creates Automatically?
The goal of Automatically Generated Data Tests is to cast a wide net for data problems that can’t be predicted by targeted testing devised in advance. It’s the same way you might set up a burglar alarm by deploying sensors at all possible entrances. Your goal in refining these tests is to maintain maximum sensitivity to real problems while minimizing false positives. Examples: Alpha Truncation, Average Shift, Constant Value Present, Daily Record Count, Value present in List-of-Values, Distinct Value Change, Future Date, Incremental Average Shift.
What Are TestGen’s 10 Types Of Custom Tests?
Business Rule Configurable Data Tests allow you to configure data quality validation tests that can’t be gleaned automatically from prior data. It is faster and easier to set up Business Rule Configurable Data Tests than to program custom SQL. Business Rule and Data Test logic are already programmed, tested, and verified to work. Examples include: Data Match, Prior Match, Aggregate Match No Drops.
How Do I Go From Data Profiling And Testing to End-to-End Data Observability?
Data quality testing is the start, not the end. DataKitchen Open Source DataOps Observability monitors every data journey—from source to customer value—so that problems are detected, localized, and understood immediately.
Who Are You Guys?
Transparent Pricing
The Enterprise version is just $100 per user per connection. Predictable costs that scale reasonably. No opaque pricing or surprise increases.
No Venture-Backed Uncertainty
We won’t suddenly pivot pricing models or sunset features. We are a profitable, stable, reliable partner committed to customer success.
AI Drives The Brutal Math Of Manual Data Quality
The terrifying truth is that AI amplifies data quality and data observability failures exponentially. A single schema drift that once meant a broken report now means thousands of incorrect predictions per second. That missing data validation you postponed? It just trained your model to be confidently wrong at scale.
Download the free guide to AI Data Quality now