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What is pytest?

Pytest serves as an essential resource for improving your coding abilities, enabling the easy development of both simple tests and more intricate functional tests across a variety of applications and libraries. The framework excels in offering comprehensive assertion introspection, allowing you to depend solely on standard assert statements for all your testing requirements. It provides extensive insights into failed assertions, automatically detects test modules and functions, and includes versatile fixtures that efficiently manage both small and parameterized long-lived test resources. Moreover, pytest can effortlessly run unittest (including trial) and nose test suites, and it supports Python versions 3.6 and later, as well as PyPy 3. Its extensive plugin ecosystem includes over 315 external plugins, alongside a dynamic community of users contributing to its growth. Additionally, the maintainers of pytest, in collaboration with Tidelift, offer commercial support and maintenance for the open-source dependencies crucial to your projects, further enhancing its value. By integrating pytest into your workflow, you can streamline your testing process, reduce potential risks, and improve the overall quality of your codebase, all while ensuring fair compensation for the developers of the dependencies you utilize. This dedication to community engagement and support distinctly positions pytest as a frontrunner in the realm of testing frameworks, making it a preferred choice among developers.

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

Allure Report
Apache Spark
Captain
Codecov
Coverage.py
Katalon Recorder
Katalon TestCloud
Launchable
Opik
Pynt
Python
Roost.ai
TestQuality
Testmo
VIKTOR
Zebrunner
pytest-cov

Integrations Supported

Allure Report
Apache Spark
Captain
Codecov
Coverage.py
Katalon Recorder
Katalon TestCloud
Launchable
Opik
Pynt
Python
Roost.ai
TestQuality
Testmo
VIKTOR
Zebrunner
pytest-cov

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
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

pytest

Date Founded

2004

Company Website

docs.pytest.org/en/6.2.x/

Company Facts

Organization Name

Deequ

Company Website

github.com/awslabs/deequ

Categories and Features

Functional Testing

Automated Testing
Interface Testing
Regression Testing
Reporting / Analytics
Sanity Testing
Smoke Testing
System Testing
Unit Testing

Software Testing

Automated Testing
Black-Box Testing
Dynamic Testing
Issue Tracking
Manual Testing
Quality Assurance Planning
Reporting / Analytics
Static Testing
Test Case Management
Variable Testing Methods
White-Box Testing

Categories and Features

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