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Great Expectations

The open standard for data quality.

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Overview

Great Expectations is an open-source Python library that helps data teams to eliminate pipeline debt, through data testing, documentation, and profiling. It allows you to define 'expectations' about your data, which are then used to validate new data as it enters your pipelines. Great Expectations helps you to catch data quality issues early and to maintain a shared understanding of your data.

✨ Key Features

  • Data testing and validation
  • Automated data documentation
  • Data profiling
  • Extensible and customizable
  • Support for a wide range of data sources

🎯 Key Differentiators

  • Open source and highly extensible
  • Focus on data testing and documentation
  • Strong community support

Unique Value: Provides an open and flexible framework for data quality, enabling data teams to build robust and reliable data pipelines.

🎯 Use Cases (4)

Data quality testing in ETL/ELT pipelines Data validation for machine learning models Automated data documentation and cataloging Data quality monitoring

✅ Best For

  • Ensuring data quality in data science workflows
  • Validating data in production data pipelines
  • Creating a living document of data quality expectations

💡 Check With Vendor

Verify these considerations match your specific requirements:

  • End-to-end data observability
  • Data governance and access control

🏆 Alternatives

dbt tests Soda Monte Carlo

Offers a more programmatic and developer-friendly approach to data quality compared to GUI-based tools.

💻 Platforms

Python library

✅ Offline Mode Available

🔌 Integrations

Pandas Spark SQLAlchemy dbt Airflow Prefect

💰 Pricing

Contact for pricing
Free Tier Available

Free tier: Fully-featured open source version.

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