Great Expectations vs Apache Griffin

Great Expectations7.8Visit AGApache Griffin7.3Visit

Higher score Great Expectations #7 in ETL Testing Tools 7.8/10 Free plan Free plan✓ 2 of 4 features Visit Great Expectations

vs

AG Apache Griffin #8 in ETL Testing Tools 7.3/10 Free (open source) Free plan✓ 1 of 4 features Visit Apache Griffin

• Great Expectations fits best

  Engineering teams that want validation defined and versioned as code, for free

• Apache Griffin fits best

  Big-data teams on Spark/Hadoop who want validation with zero license cost

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Side by side

Feature	Great Expectations 7.8/10 Visit ↗	AGApache Griffin 7.3/10 Visit ↗
At a glance
Editor score	7.8	7.3
Ranking	#7 in ETL Testing Tools	#8 in ETL Testing Tools
Best for	Engineering teams that want validation defined and versioned as code, for free	Big-data teams on Spark/Hadoop who want validation with zero license cost
Pricing model	Free plan + paid	Free
Starting price	Free plan	Free (open source)
Free plan	✓	✓
Free trial	—	—
Features Great Expectations 2/4 · Apache Griffin 1/4
Source-to-target reconciliation	—	✓ (best)
No-code test authoring	—	—
CI/CD integration	✓ (best)	—
API access	✓ (best)	—
Specs
Deployment model	Self-hosted	Self-hosted
Our review
Pros	No licensing cost for the open-source core, and it's widely used so answers are easy to find Its validation assertions read as plain English and double as documentation of what the data should look like Integrates into most Python-based pipelines (Airflow, dbt, pandas, Spark) without a proprietary agent	No licensing cost at all, under the Apache Software Foundation Built on Spark, so it scales the same way the big-data pipelines it checks already do Supports both batch and streaming data quality measurement
Cons	No GUI for non-engineers; writing and maintaining expectations assumes comfort with Python Self-hosting means your own team owns scaling, uptime, and upgrades GX Cloud (the hosted option) is a separate product with its own evaluation, not just "the paid tier"	No vendor support line; help comes from community docs and mailing lists, not a support contract Setup and ongoing maintenance assume real familiarity with Spark and the Hadoop ecosystem Smaller, slower-moving community than more actively marketed commercial tools on this list
Our verdict	Great Expectations is the tool we'd point a Python-comfortable data engineering team to when the budget line for ETL testing is zero but the standards still need to be real. "Expectations" are declared as code, which means they're… Read the review →	Apache Griffin is the choice for a team that's already standardized on Spark for big-data processing and wants data quality and ETL validation to live in that same stack instead of adding a new vendor and a new connector to maintain. It… Read the review →

Strengths and trade-offs

• Great Expectations — where it wins

  • No licensing cost for the open-source core, and it's widely used so answers are easy to find
  • Its validation assertions read as plain English and double as documentation of what the data should look like
  • Integrates into most Python-based pipelines (Airflow, dbt, pandas, Spark) without a proprietary agent

  Where it doesn't

  • No GUI for non-engineers; writing and maintaining expectations assumes comfort with Python
  • Self-hosting means your own team owns scaling, uptime, and upgrades
  • GX Cloud (the hosted option) is a separate product with its own evaluation, not just "the paid tier"

• Apache Griffin — where it wins

  • No licensing cost at all, under the Apache Software Foundation
  • Built on Spark, so it scales the same way the big-data pipelines it checks already do
  • Supports both batch and streaming data quality measurement

  Where it doesn't

  • No vendor support line; help comes from community docs and mailing lists, not a support contract
  • Setup and ongoing maintenance assume real familiarity with Spark and the Hadoop ecosystem
  • Smaller, slower-moving community than more actively marketed commercial tools on this list

• Great Expectations7.8/10 · Free plan

  The default open-source choice for teams that want validation as Python code, not a GUI.

  Visit Great ExpectationsFull verdict →
• AGApache Griffin7.3/10 · Free (open source)

  A no-cost, self-hosted option for teams already running Spark who want data quality checks in the same stack.

  Visit Apache GriffinFull verdict →