Their Pitch
Everything you need to trust your data.
Our Take
It's unit testing for your data - catches bad numbers before they break your reports or make your CEO ask uncomfortable questions.
Deep Dive & Reality Check
Used For
- +**Your sales dashboard shows negative revenue and your CEO panicked** → Catches impossible values before they hit executive reports
- +**Spending 2 hours every Monday hunting for data errors in reports** → Automated checks run overnight, flag problems before anyone sees them
- +**Pipeline crashes when someone uploads a file with extra columns** → Handles schema changes gracefully instead of breaking everything
- +200+ pre-built rules for common problems - no coding "expect customer_id to never be empty" from scratch
- +Auto-suggests 10-50 data quality checks by scanning your actual data patterns
Best For
- >Your weekly reports keep having obvious errors and your boss is asking questions
- >Data pipelines break at 3am and you're tired of weekend emergency fixes
- >You're manually spot-checking spreadsheets for hours because you don't trust the numbers
Not For
- -Small teams working with simple spreadsheets - this is overkill for basic CSV validation
- -Non-technical teams without Python skills - the free version requires actual coding
- -Solo analysts who just need to clean data once - you'll spend more time setting it up than fixing manually
Pairs With
- *Airflow (runs your data quality checks as part of the pipeline before loading to warehouse)
- *Snowflake (where your clean data actually lives after passing validation)
- *dbt (transforms data after GX confirms it's not garbage)
- *Slack (where you get alerts when validation fails at 2am)
- *Pandas (for the actual data processing that GX is checking)
- *Tableau (displays the data you now trust because GX validated it)
- *AWS S3 (stores the validation results and metadata)
The Catch
- !The free version requires Python coding and manual setup - no pretty dashboards without paying
- !Cloud pricing isn't public, which usually means "expensive enough that we don't want to scare you away upfront"
- !You'll need someone to maintain the rules as your data changes, or they'll become useless noise
Bottom Line
Like spell-check for your data pipeline - catches the typos before they become disasters.