Great Expectations
Open-source data quality validation framework
Shyft Score
Directory quality rating
73/100
Quiet
Our take
The verdict on Great Expectations
AI-generated
Great Expectations is a powerful open-source tool for data validation and profiling, ideal for teams that need customizable data quality checks without vendor lock-in.
Strengths
- Free and open-source
- Customizable data validation
- Supports SQL and NoSQL databases
Limitations
- Lacks real-time monitoring
- No AI agent support
Best for: Data teams needing open-source data validation and profiling
Try Great Expectations's free tier to see if it fits your workflow.
See how Great Expectations fits your stackAbout
What is Great Expectations?
Open-source data quality framework for validating, documenting, and profiling data. Define expectations as tests that run against your data pipelines.
Key capabilities
Data validation through customizable expectations
Automated documentation generation
Data profiling capabilities to understand data distributions
Integration with popular data orchestration tools
Support for various data sources including SQL and NoSQL databases
At a glance
Use cases
What you can do with Great Expectations
Validate data quality for a machine learning model by setting up automated data checks
Monitor data pipeline performance and detect anomalies in real-time
Document data quality expectations and track their evolution over time
Best for
Data engineersData scientistsData analysts
Pricing
Great Expectations pricing
Great Expectations starts at $0
Starting at $0
Ecosystem
Connected ecosystem
MCP servers, AI skills, and integrations that work with Great Expectations
Integrations
Apache AirflowdbtSnowflakeAmazon RedshiftGoogle BigQueryPostgreSQLMySQLPandasApache Spark
FAQs
Frequently asked questions
Common questions about Great Expectations and its capabilities
Great Expectations pricing starts at $0. Contact Great Expectations for enterprise pricing and volume discounts.
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