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Description

Deequ is an innovative library that extends Apache Spark to create "unit tests for data," aiming to assess the quality of extensive datasets. We welcome any feedback and contributions from users. The library requires Java 8 for operation. It is important to note that Deequ version 2.x is compatible exclusively with Spark 3.1, and the two are interdependent. For those using earlier versions of Spark, the Deequ 1.x version should be utilized, which is maintained in the legacy-spark-3.0 branch. Additionally, we offer legacy releases that work with Apache Spark versions ranging from 2.2.x to 3.0.x. The Spark releases 2.2.x and 2.3.x are built on Scala 2.11, while the 2.4.x, 3.0.x, and 3.1.x releases require Scala 2.12. The primary goal of Deequ is to perform "unit-testing" on data to identify potential issues early on, ensuring that errors are caught before the data reaches consuming systems or machine learning models. In the sections that follow, we will provide a simple example to demonstrate the fundamental functionalities of our library, highlighting its ease of use and effectiveness in maintaining data integrity.

Description

The unittest framework for unit testing was influenced by JUnit and shares characteristics with other prominent unit testing frameworks across various programming languages. It offers features like test automation, the ability to share setup and teardown procedures, the grouping of tests into collections, and ensures that tests operate independently from the reporting framework. A test fixture is essential for preparing the environment required for one or more tests, along with any necessary cleanup processes, which might include setting up temporary databases, creating directories, or initiating server processes. A test suite serves as a compilation of test cases and other test suites, designed to group tests that should be run together. Meanwhile, a test runner acts as a mechanism to manage the execution of tests and communicate the results to the user. This runner can function through a graphical interface, a command-line interface, or may return a specific value to reflect the outcomes of the tests executed. Overall, the unittest framework simplifies the testing process while promoting organized and efficient test execution.

API Access

Has API

API Access

Has API

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Integrations

Apache Spark
Captain
Codecov
Python

Integrations

Apache Spark
Captain
Codecov
Python

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

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Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Deequ

Website

github.com/awslabs/deequ

Vendor Details

Company Name

Python

Website

docs.python.org/3/library/unittest.html

Product Features

Product Features

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Alternatives

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