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What is unittest?

The unittest framework, designed for unit testing, draws inspiration from JUnit and shares similarities with other well-known unit testing frameworks used in different programming languages. It provides a variety of features, including automated testing, shared setup and teardown methods, and the organization of tests into logical collections, ensuring that each test runs independently of the reporting system. A crucial component of the framework is the test fixture, which prepares the necessary environment for one or more tests and handles any required cleanup tasks, such as establishing temporary databases, creating directories, or starting server processes. Additionally, a test suite aggregates multiple test cases and other suites, helping to organize tests that need to be executed together. The test runner, meanwhile, is responsible for overseeing the execution of tests and relaying the results back to the user, functioning through various interfaces like graphical or command-line options, and potentially returning specific values to indicate the results of the tests performed. By streamlining the testing process and fostering organized test execution, the unittest framework not only enhances efficiency but also encourages better software quality through consistent testing practices. Its versatility makes it an essential tool for developers looking to ensure their code is robust and reliable.

What is Deequ?

Deequ is a groundbreaking library designed to enhance Apache Spark by enabling "unit tests for data," which helps evaluate the quality of large datasets. User feedback and contributions are highly encouraged as we strive to improve the library. The operation of Deequ requires Java 8, and it is crucial to recognize that version 2.x of Deequ is only compatible with Spark 3.1, creating a dependency between the two. Users of older Spark versions should opt for Deequ 1.x, which is available in the legacy-spark-3.0 branch. Moreover, we also provide legacy releases that support Apache Spark versions from 2.2.x to 3.0.x. The Spark versions 2.2.x and 2.3.x utilize Scala 2.11, while the 2.4.x, 3.0.x, and 3.1.x releases rely on Scala 2.12. Deequ's main objective is to conduct "unit-testing" on data to pinpoint possible issues at an early stage, thereby ensuring that mistakes are rectified before the data is utilized by consuming systems or machine learning algorithms. In the upcoming sections, we will illustrate a straightforward example that showcases the essential features of our library, emphasizing its user-friendly nature and its role in preserving data quality. This example will also reveal how Deequ can simplify the process of maintaining high standards in data management.

Media

Media

Integrations Supported

Apache Spark
Captain
Codecov
Python

Integrations Supported

Apache Spark
Captain
Codecov
Python

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Python

Company Website

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

Company Facts

Organization Name

Deequ

Company Website

github.com/awslabs/deequ

Categories and Features

Categories and Features

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