This page compares the tools. The BigQuery how-to covers setting up TestGen, step by step.
What BigQuery gives you natively
Google’s answer is Knowledge Catalog, which was Dataplex Universal Catalog until April 2026. Its auto data quality scans run built-in row rules (range, null, set, regex), aggregate rules (uniqueness, statistics), and custom SQL assertions against BigQuery and Iceberg REST Catalog tables. Data profiling computes null rates, distinct counts, top values, and quartiles, and it can recommend rules. Scans run on a schedule, results land in Cloud Logging, and email goes out when a score drops below target.
It’s managed, it’s already in your console, and the scans bill in Data Compute Units on your Google invoice. That’s a real advantage.
The limit is authorship. Recommendations are a starting point, and you still pick, tune, and attach rules table by table, up to 1,000 per scan. A profile scan won’t even be created on a table with a BIGNUMERIC column. And nothing in the scan watches whether a table stopped growing or arrived late unless you write that rule too. (Google’s docs, checked October 21, 2026.)
Tools worth considering on BigQuery
| Tool | Profiling | Generated data tests | Observability monitors | Data quality dashboards | Runs in-platform | Open source | Price basis |
|---|---|---|---|---|---|---|---|
| DataOps TestGen | 55 characteristics, 32 hygiene checks | Yes, 34 test types | Freshness, volume, schema, custom metric | Scorecards, CDE score | SQL in BigQuery, self-hosted app | Apache 2.0 | Free for one connection; $100/user + $100/connection per month |
| Knowledge Catalog (Dataplex) | Yes | Recommends rules from a profile | Scheduled rule scans | Quality scores by dimension | Google-managed, serverless | No | Data Compute Units |
| Monte Carlo | Yes | Partial | ML freshness and volume, schema, query logs | Yes | SaaS, read-only service account | No | Credits per monitor, on request |
| Soda | Yes (Cloud) | Partial | Metric monitoring, anomaly detection | Yes (Cloud) | Soda-hosted or self-hosted runner | Soda Core, Elastic License 2.0 | Free tier; Team $750/month |
| Elementary | Partial (OSS) | No (OSS) | dbt anomaly tests | Self-hosted report | dbt package in your project | Apache 2.0 | OSS free; Cloud on request |
Generated-test and profiling ratings for Monte Carlo, Soda, and Elementary come from DataKitchen’s comparison pages, last reviewed June to October 2026. Vendor pages checked October 21, 2026.
Who should pick what
Pick Monte Carlo if you want a managed service with ML anomaly detection, query-log analysis, and field-level lineage across BigQuery and the tools around it, and you have the budget. DataKitchen’s Monte Carlo comparison estimates $120K to $250K a year for 10 users and 1,000 tables. That’s our estimate, not their quote. Pick Elementary if your whole pipeline is dbt and you want tests next to the models. Stay with Knowledge Catalog if governance owns the rules and you only need a few dozen of them.
Pick TestGen if you have hundreds of tables and nobody to write their tests. It profiles every column, writes the tests, and runs them as SQL in BigQuery, so the data never crosses the wire. The October 2025 release added BigQuery. We counted the vendors in this market once. Most of them price per table.
The core profiling, testing, and monitoring engine is free and open source. Enterprise adds team access, security, Commercial Pharma tests, source-data controls for AI, and support. See the supported sources and the Google Cloud overview.
Documentation: Connect your Data, Access Requirements, Manage Table Groups, Data quality quickstarts (no BigQuery quickstart yet).