Deequ
Built on Apache Spark, Deequ enables users to define "unit tests for data" that assess data quality in large datasets. It verifies assumptions about data attributes, ensu... Built on Apache Spark, Deequ enables users to define "unit tests for data" that assess data quality in large datasets. It verifies assumptions about data attributes, ensuring integrity before processing. Ideal for distributed environments, Deequ empowers data professionals to identify and resolve errors early, enhancing overall data reliability.
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Company Information
- Company: GitHub
- Country: United States
Top Deequ Features
- Data quality unit tests
- Apache Spark integration
- Supports large datasets
- Quarantine erroneous data
- Define data assumptions
- Metrics computation on data
- VerificationSuite entry point
- Customizable data checks
- Python interface available
- Legacy version compatibility
- Detects NULL values
- Assertion functions for constraints
- Easily integrate with Maven
- Scalable data validation
- Feedback-driven development
- Automated data quality checks
- Inspectable verification results
- Advanced functionality support
- Documentation for qualifiers
- Community contributions encouraged
