Find AI Tools
Great Expectations logo

Great Expectations

Open-source data quality testing framework for building reliable data pipelines. Pricing: Freemium (Open source free; GX Cloud pricing available). See pros, cons, alternatives, and comparisons.

Get Great Expectations
  • Open source
  • Free forever
Great Expectations screenshot

What is Great Expectations?

Great Expectations is an open-source framework designed to help teams validate data quality and build confidence in their data pipelines. It works by letting you define expectations about what your data should look like, then automatically tests incoming data against those rules. This catches problems early, before bad data causes downstream issues in analytics, machine learning models, or business processes. The tool is built for data engineers, analysts, and teams managing data pipelines at any scale. You can write expectations in plain language or Python, run them as part of your data workflow, and get clear reports on what passed and what failed. Great Expectations integrates with popular data platforms like Pandas, SQL databases, Spark, and cloud data warehouses. The core framework is free and open-source. For teams wanting managed infrastructure, monitoring dashboards, and centralised data quality oversight, Great Expectations offers GX Cloud as a paid service.

Key features

Expectation definitions

Create data quality rules using simple syntax or Python to specify what valid data should look like

Automated testing

Run validations automatically as part of your pipeline to catch data issues before they propagate

Multiple data source support

Works with Pandas dataframes, SQL databases, Spark, Snowflake, BigQuery, and other data platforms

Data documentation

Automatically generate documentation about your data quality checks and historical validation results

Validation reporting

Get detailed reports showing which expectations passed, failed, or had warnings

Open-source framework

Full control over your code with no vendor lock-in; run it on your own infrastructure

Pros & cons

Advantages

  • Free and open-source; no licensing costs for the core framework
  • Works with multiple data sources and warehouse platforms without requiring database-specific configuration
  • Clear, readable expectations that non-technical stakeholders can understand
  • Good community support and documentation for the open-source version
  • Catches data quality issues early in the pipeline, reducing downstream problems

Limitations

  • Setup and configuration require technical knowledge; not a point-and-click tool for non-technical users
  • Managing expectations across large, complex pipelines can become difficult without additional tooling or processes
  • GX Cloud pricing is opaque and available only by request, making budget planning uncertain

Use cases

Data engineering teams validating data before it enters a data warehouse or lake

Analytics teams ensuring data quality in reporting pipelines to prevent incorrect dashboards

Machine learning teams checking input data quality before model training

Data governance; documenting data quality standards and compliance checks

Migrating data between systems and verifying completeness and correctness after the move

Ready to try Great Expectations?

Get Great Expectations

Pricing

Open Source

Free

Full Great Expectations framework for building and running data quality tests on your own infrastructure

GX Cloud

Custom pricing

Managed cloud service with hosted validation runs, centralised monitoring dashboards, team collaboration features, and data cataloguing

Get started with Great Expectations

Click through to Great Expectations and start using it now.

Get Great Expectations
  • Open source
  • Free forever