Deequ
Library built on top of Apache Spark for defining 'unit tests for data', which measure data quality in large datasets.
Deequ is a library built on top of Apache Spark for defining "unit tests for data" to measure data quality in large datasets. Developed by Amazon and open-sourced, Deequ provides a programmatic API for expressing data quality constraints and computing metrics on large datasets.
The library is designed for data engineers and scientists who work with big data and need to ensure data quality at scale. Deequ integrates seamlessly with Spark workflows and can handle datasets of any size that Spark can process. It's particularly powerful for organizations already using Spark for their data processing pipelines.
Deequ uses incremental computation to efficiently calculate data quality metrics, making it suitable for continuous monitoring scenarios. The library supports both Scala and Python APIs, making it accessible to different development teams.
Unit Tests for Data
Define data quality constraints as code with verification and suggestion APIs
Incremental Computation
Efficient metric calculation that scales with data size
Anomaly Detection
Built-in algorithms to detect unusual patterns in data quality metrics
Profiling Capabilities
Automatic data profiling to understand dataset characteristics
Constraint Suggestions
ML-powered suggestions for appropriate data quality constraints
Repository Pattern
Store and track data quality metrics over time for trend analysis
- •Free and open source with Apache 2.0 license
- •Seamless integration with existing Spark workflows
- •Scales to handle massive datasets efficiently
- •Programmatic API allows for flexible integration
- •Backed by Amazon with proven production use
- •Supports both Scala and Python ecosystems
- •Requires Spark expertise and infrastructure
- •No built-in UI for non-technical users
- •Limited documentation and community resources
- •Primarily code-based approach may not suit all teams
- •Setup and configuration can be complex for beginners
- •Limited real-time streaming capabilities
Spark Users
Teams already using Apache Spark for data processing
Big Data Teams
Organizations processing large-scale datasets
AWS Users
Teams using AWS data services and EMR
Amazon Retail
E-commerce
"Deequ provides scalable unit tests for data, helping us maintain quality across massive retail datasets. It's integrated into our Spark pipelines and catches data issues before they impact customer experience."
Source: github.com