Install open source DataOps TestGen
Open source data quality, installed in minutes.
Never write a data test again. TestGen profiles your warehouse, generates thousands of tests, and flags data hygiene issues on the first run. Full UI, complete MCP, runs unlimited in your environment.
Install TestGen
Pick your platform. Free and open source.
# TestGen install for Mac, Linux.
# Download the latest installer.
$ curl -o dk-installer.py \
'https://raw.githubusercontent.com/DataKitchen/data-observability-installer/main/dk-installer.py'
# Run the install command.
$ python3 dk-installer.py tg install
# Full docs: docs.datakitchen.io/testgen/get-started/install-on-mac-linux/- Double-click
dk-installer.exein your Downloads folder. - If Windows shows a protection warning, choose More info, then Run anyway.
- Pick TestGen, then Docker or pip. The installer seeds demo data, opens the UI, and prints your login.
# Full docs: docs.datakitchen.io/testgen/get-started/install-on-windows/
- 55data profiling column characteristics
- 32data hygiene detectors
- 34test types auto-generated from the profile
- 19business rule and custom SQL test types
The periodic table of data quality
TestGen's checks, sorted by the data quality dimension each one protects.
- Custom Test Accuracy (documentation, opens in a new tab)
- SQL Metric Accuracy (documentation, opens in a new tab)
- New Shift Accuracy (documentation, opens in a new tab)
- Outliers Above Accuracy (documentation, opens in a new tab)
- Outliers Below Accuracy (documentation, opens in a new tab)
- Daily Records Completeness (documentation, opens in a new tab)
- Percent Missing Completeness (documentation, opens in a new tab)
- Monthly Records Completeness (documentation, opens in a new tab)
- No Column Values Present Completeness (documentation, opens in a new tab)
- Required Entry Completeness (documentation, opens in a new tab)
- Required, No Placeholder Completeness (documentation, opens in a new tab)
- Row Count Completeness (documentation, opens in a new tab)
- Row Range Completeness (documentation, opens in a new tab)
- Weekly Records Completeness (documentation, opens in a new tab)
- Volume Completeness (documentation, opens in a new tab)
- Non-standard Blank Values Completeness (documentation, opens in a new tab)
- Small Percentage of Missing Values Completeness (documentation, opens in a new tab)
- Aggregate Minimum Consistency (documentation, opens in a new tab)
- Aggregate Balance Consistency (documentation, opens in a new tab)
- Average Shift Consistency (documentation, opens in a new tab)
- Multiple Data Types per Column Name Consistency (documentation, opens in a new tab)
- Distribution Shift Consistency (documentation, opens in a new tab)
- Timeframe No Drops Consistency (documentation, opens in a new tab)
- Timeframe Match Consistency (documentation, opens in a new tab)
- Value Match All Consistency (documentation, opens in a new tab)
- Constant Match Consistency (documentation, opens in a new tab)
- Custom Condition Consistency (documentation, opens in a new tab)
- Reference Match Consistency (documentation, opens in a new tab)
- Decimal Truncation Consistency (documentation, opens in a new tab)
- Value Match Consistency (documentation, opens in a new tab)
- Date Count Timeliness (documentation, opens in a new tab)
- Past Dates Timeliness (documentation, opens in a new tab)
- Future Year Timeliness (documentation, opens in a new tab)
- Recency Timeliness (documentation, opens in a new tab)
- Table Freshness Timeliness (documentation, opens in a new tab)
- Potential Duplicate Values Uniqueness (documentation, opens in a new tab)
- Unique Values Uniqueness (documentation, opens in a new tab)
- Percent Unique Uniqueness (documentation, opens in a new tab)
- Duplicate Rows Uniqueness (documentation, opens in a new tab)
- Leading Spaces Validity (documentation, opens in a new tab)
- Pattern Inconsistency Within Column Validity (documentation, opens in a new tab)
- Alpha Truncation Validity (documentation, opens in a new tab)
- Value Count Validity (documentation, opens in a new tab)
- Email Format Validity (documentation, opens in a new tab)
- Valid US Zip Validity (documentation, opens in a new tab)
- Minimum Date Validity (documentation, opens in a new tab)
- Minimum Value Validity (documentation, opens in a new tab)
- Maximum Date Validity (documentation, opens in a new tab)
- Maximum Value Validity (documentation, opens in a new tab)
- Text Length Range Validity (documentation, opens in a new tab)
- Character Column with Mostly Date Values Validity (documentation, opens in a new tab)
- Character Column with Mostly Numeric Values Validity (documentation, opens in a new tab)
- Small Percentage of Divergent Values Validity (documentation, opens in a new tab)
- Pattern Match Validity (documentation, opens in a new tab)
- Street Address Validity (documentation, opens in a new tab)
- Suggested Data Type Validity (documentation, opens in a new tab)
- Unexpected Boolean Values Validity (documentation, opens in a new tab)
- US State Validity (documentation, opens in a new tab)
- Metric Trend Validity (documentation, opens in a new tab)
- Commercial Pharma (12 tests, Enterprise) Validity (documentation, opens in a new tab)
Showing all 71 checks. Each tile links to its section in the TestGen documentation.
Key capabilities
Everything below is in the open source release. No feature gating, no upgrade prompt.
-
Auto-Generated Tests (documentation, opens in a new tab)
Point TestGen at your data and it generates the tests automatically. Profiling, validation, and execution included. No coding or YAML configuration required.
-
Data Profiling (documentation, opens in a new tab)
Periodic X-ray of every table and column in your database. 55 column characteristics analyzed, stored, and available for review and downstream rules derivation.
-
Hygiene Detection (documentation, opens in a new tab)
Automatically confirm how closely data structures and assumptions match actual column contents. Identify problem rows before they reach production.
-
Anomaly Detection (documentation, opens in a new tab)
Four monitor types: freshness, volume, schema, and custom metrics. Predictive models learn what your data usually looks like and flag late arrivals, row count swings, schema drift, and metric anomalies before they reach downstream consumers.
-
Data Catalog (documentation, opens in a new tab)
Every table's metadata in one place. See profile results, hygiene issues, test results, PII identification, and Critical Data Element tagging.
-
Quality Scoring (documentation, opens in a new tab)
Configurable dashboards and scorecards based on DAMA categories, Critical Data Elements, specific model requirements, or current business goals.
-
Full-Featured, Open Source (source code on GitHub, opens in a new tab)
No crippleware. Unlimited table usage. UI included. All the features that you see on this page are included in the Apache 2.0 open source.
-
MCP Server (the TestGen MCP cheat sheet)
Connect Claude, Claude Code, Cursor, Copilot, or Databricks Genie to TestGen's 96 MCP tools. Ask what failed, see the rows behind it, and approve fixes in plain English. It runs behind your firewall.
Works where your data lives
-
Snowflake -
Databricks -
DuckDB -
Azure Synapse -
Azure SQL -
SQL Server -
BigQuery -
Redshift -
Oracle -
SAP HANA -
PostgreSQL
Also supported: Amazon Aurora PostgreSQL, Microsoft OneLake, and Salesforce Data 360. CSV, Parquet, and JSON files, read directly from a local path, Amazon S3, and Azure Blob Storage. Plus structured data in Apache Iceberg tables and file formats (Parquet, Avro, ORC, CSV, JSON) exposed as external tables, including Amazon Redshift Spectrum and Snowflake external tables.
Why teams choose TestGen
-
You Don't Have Time to Write Tests. TestGen Does It Automatically.
You're buried in customer requests. You have no time to write tests, let alone innovate. TestGen scans your data and generates the tests and anomaly detectors for you. No coding, no massive YAML configuration.
Open Source with Reasonable Enterprise Pricing
Enterprise pricing is a flat $100 per month per user and database connection, with unlimited tables and data volume. No per-table tax. A year of enterprise data quality costs roughly one data engineer's monthly salary.
See pricingTestGen versus the competition
Head-to-head comparisons with every major data quality and observability tool. Search for the one you are evaluating.
No vendors match. .
- Bristol Myers Squibb
- Eisai
- Catholic Relief Services
- Progeny Health
- X4
- KOA
- CNH
Free book
Not ready to try it out now? Read the free book.
The DataOps Way to Data Quality and Data Observability covers why data quality keeps failing and what to do about it: culture, testing strategy, monitoring, and the seven deadly sins of AI-generated pipelines.
- 620 pages
- 63 chapters
- Updated September 2026
- By Chris Bergh, Chip Bloche, and Gil Benghiat