Great Expectations
Open-source data validation framework that helps teams eliminate pipeline debt through data testing, documentation, and profiling.
Great Expectations is the leading open-source Python-based data validation framework. It helps data teams eliminate pipeline debt through data testing, documentation, and profiling. The framework allows you to assert what you expect from your data, and it will tell you whether your expectations are met.
With Great Expectations, you can create expectations (assertions) about your data, validate data against those expectations, and generate data documentation automatically. It's designed to be human-readable and machine-executable.
Vimeo Analytics
Media & Entertainment
"Great Expectations helps us test data pipelines and ensure data quality across our analytics infrastructure."
Source: greatexpectations.io
Data Validation
Create and run data quality tests with over 300 built-in expectations
Automated Testing
Integrate data tests into your CI/CD pipeline
Data Docs
Automatically generate beautiful, human-readable documentation
Data Profiling
Understand your data structure and quality metrics
- •Highly customizable with extensive expectation library
- •Strong community support and active development
- •Excellent documentation and learning resources
- •Integrates with major data platforms and tools
- •Free and open source
- •Steep learning curve for beginners
- •Initial setup can be complex
- •Requires Python knowledge
- •Can be resource-intensive for large datasets
Open Source
Free
Full access to the Great Expectations framework with community support
- • All core features
- • Community support
- • GitHub repository access
- • Documentation and tutorials
Great Expectations Cloud
Contact Sales
Managed service with additional enterprise features
- • Hosted infrastructure
- • Enterprise support
- • Advanced collaboration features
- • SSO and security controls
Data Engineers
Building robust data pipelines with automated testing
Data Scientists
Ensuring data quality for ML models and analysis
Data Teams
Organizations wanting open-source flexibility
Time to Value
1-2 weeks for basic setup
Technical Requirements
Python 3.7+, pandas, SQL database
Implementation Complexity
Moderate - requires Python knowledge
Required Expertise
Data engineers, Python developers
Onboarding Support
Extensive docs, tutorials, community
Learning Curve
Steep initially, powerful once mastered
Native Integrations
Snowflake, BigQuery, Redshift, Postgres, Spark
API Quality
Comprehensive Python API, well-documented
Data Export/Import
JSON, HTML reports, programmatic access
Webhook Support
Custom notifications via Python
Pre-built Connectors
20+ database connectors included
Custom Development
Highly extensible, custom expectations
Implementation Cost
$10K-50K in developer time
Ongoing Maintenance
Low - stable open source project
Contract Terms
Open source - no contracts
ROI Timeline
3-6 months for data quality improvements
vs Alternatives
90% cost savings vs enterprise tools