Great Expectations
UnclaimedEnsure governance and trust in AI with robust data quality across your pipelines.
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Tracked since2026
0 reviews trackedThe Bottom Line
Entry price
Free plan available, paid tiers above
Biggest pro
Catches data problems early in the pipeline
Biggest con
Specific pricing details for Team and Enterprise solutions are not publicly available, requiring direct engagement to understand costs.
TL;DR - Great Expectations
- Ensures data quality and governance across pipelines.
- Provides tools for data validation, monitoring, and collaboration.
- Offers both an open-source framework and a cloud platform with AI-powered features.
Pricing: Free plan available
Best for: Growing teams
What is Great Expectations?
Great Expectations (GX) is a data quality platform designed to help data teams catch data problems early, maintain stakeholder alignment, and deliver reliable data for critical decisions. It provides tools to validate data across pipelines, establish a common language for data quality, and build trust between technical and business teams. GX aims to make data governance an everyday practice by moving beyond policy checklists to actionable governance that ensures data accuracy, transparency, and compliance at scale.
The platform offers both an open-source core and a cloud-based solution. GX Core is a flexible, Python-based framework for writing data quality tests that integrate into existing data workflows, allowing users to validate data where it lives and plug structured results into CI/CD, alerting, or dashboards. GX Cloud enhances this with features like built-in observability, collaboration tools, and automated test generation using ExpectAI, enabling real-time data health monitoring and proactive alerts before bad data causes damage. It's built for modern data systems, addressing their complexity and fragility by providing the means to identify and resolve data issues during development, before data moves downstream, and in production.
Available on: Web
Pros & Cons
Pros
- Catches data problems early in the pipeline
- Helps align technical and business teams on data quality
- Flexible and integrates with existing data workflows
- Offers both open-source and cloud solutions
- Automates data quality checks and test generation
Cons
- Specific pricing details for Team and Enterprise solutions are not publicly available, requiring direct engagement to understand costs.
- The primary focus is on data quality testing and validation, which might not encompass all aspects of a broader data governance strategy without integration with other tools.
- While built on open source, the advanced features and managed service (GX Cloud) require a commercial offering, potentially limiting the full experience for purely open-source users.
Preview
Key Features
Validate critical data across pipelinesShare a common language for data quality (Expectations)Built-in observability and collaboration tools (GX Cloud)Auto-generate tests using ExpectAIMonitor data health in real timeGet alerts before bad data causes damageIntegrates with existing data stack and CI/CDAutomate checks for consistency and compliance
Pricing Plans
Pricing checked Jun 12, 2026
Developer
Free
Team
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Enterprise
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Reviews
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Great Expectations FAQ
How does Great Expectations help ensure data quality in ETL pipelines?
Great Expectations helps ensure data quality in ETL pipelines by providing a Python-based framework for writing data quality tests. These tests integrate into existing data workflows, allowing users to validate data where it lives and plug structured results into CI/CD, alerting, or dashboards to catch problems early.
Which teams benefit most from using Great Expectations?
Data teams benefit most from using Great Expectations, as it helps them catch data problems early, maintain stakeholder alignment, and deliver reliable data for critical decisions. It is designed to build trust between technical and business teams by establishing a common language for data quality.
How is Great Expectations priced?
Great Expectations is available on a free tier, with paid plans offered for more extensive usage and advanced features. Specific pricing details for Team and Enterprise solutions are not publicly available and require direct engagement to understand costs.
Can Great Expectations automate the generation of data quality tests?
Yes, Great Expectations Cloud enhances the platform with features like automated test generation using ExpectAI. This capability helps users proactively monitor data health and receive alerts before bad data can cause damage.
What kind of limitations should users consider when adopting Great Expectations?
Users should consider that while Great Expectations excels at data quality testing and validation, it may not encompass all aspects of a broader data governance strategy without integration with other tools. Additionally, advanced features and the managed service (GX Cloud) are part of a commercial offering, which might limit the full experience for purely open-source users.
How does Great Expectations compare to dbt for data quality management?
Great Expectations focuses on providing a flexible, Python-based framework for writing and validating data quality tests across pipelines, integrating into existing data workflows. While dbt also supports data quality, Great Expectations specifically aims to establish a common language for data quality and build trust between technical and business teams through its validation capabilities.
Does Great Expectations offer both open-source and cloud-based solutions?
Yes, Great Expectations offers both an open-source core (GX Core) and a cloud-based solution (GX Cloud). GX Core provides a flexible framework for data quality tests, while GX Cloud adds features like built-in observability, collaboration tools, and automated test generation.
Source: greatexpectations.io