Build an automated data factory

Your team will spend months writing data quality tests by hand. TestGen profiles every column and generates the suite in two steps. It runs on every refresh and re-baselines for each medallion layer. Open source. Built the DataOps way.

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Writing tests takes too long. TestGen writes them for you

A 100-table Bronze layer needs about 2,200 tests. Silver needs another 2,200. Gold adds 620 plus business-metric tests. That's 14 months of full-time work, with no meetings, no breaks, no vacation. So most teams write three tests on the dashboard table and ship. TestGen profiles each table column by column, generates freshness, volume, schema, and drift tests in minutes, and re-baselines for each layer's schema. A junior operator can run it. The math stops being against you.

Test every layer of your medallion

Bronze is where bad data lands. Silver is where joins go wrong. Gold is where business-logic drift shows up. Each layer fails differently, and tests baselined on Bronze don't catch a Gold problem. TestGen re-baselines its suite at every layer transition. Add a tripwire task between transforms. When a test fails, the next transform doesn't run. Bad data stops at the layer where it broke, not at the dashboard at 9am.

“So much of what we do involves business questions that are fire drills. Executives want answers as quickly as possible. The infrastructure that we've set up with DataKitchen allows us to mix & match data in new ways so that we can quickly get the answer to a question.”

Manager, Data Engineering

Get coverage on every table, every column

Test coverage in software is mature. In data, most teams have three tests on the dashboard table and call it done. The rule of thumb: every table gets at least two tests for freshness, volume, and schema. Every column gets at least two tests on its data. Every business metric gets a custom test. Every tool in the pipeline gets a status check. That adds up to thousands of tests across a medallion. TestGen generates them and shows you which tables are covered and which aren't. You see the gaps.

The 1:10:100 cost rule

Catching a data error at the source costs about a dollar a record. Catching it after transformation costs about ten. Letting a customer find it costs about a hundred. The numbers come from George Labovitz and Yu Sang Chang in 1992 and they haven't gotten any cheaper. Shift left: run TestGen at the Bronze ingestion layer where the cost is low. Stop fighting fires at the dashboard where it's high and the VP is on the phone.

The DataOps way to data engineering

Tests are infrastructure. So is the team that runs them. The DataOps Cookbook lays out the methodology, free to read online, with chapters on environments, orchestration, testing, and the org practices that make it stick. The DataOps 101 course is free and you can finish it in an afternoon. The Data Observability and Data Quality certification is free too. Read first, run TestGen second. The 2am Slack threads stop.