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What is Jasmine?

Jasmine is committed to following the principles of semantic versioning, which involves assigning major version numbers (like 1.0, 2.0, etc.) when there are substantial changes or breaking modifications. Typically, Jasmine's updates are classified as minor releases (such as 2.3, 2.4, etc.), with major versions being uncommon occurrences. Generally, Jasmine maintains support for specific browser or Node versions, with exceptions being made only during major version updates. However, exceptions arise when dealing with Node versions that have reached their end of life, browsers that can no longer be installed or tested within our continuous integration processes, those that no longer receive security patches, and browsers that rely solely on outdated operating systems. While we make every effort to keep Jasmine operational in these scenarios, we cannot promise a major release if any compatibility challenges arise. This strategy helps us effectively balance the need for innovation with the importance of reliability for our users. Furthermore, we are always open to feedback and suggestions from our community to improve our versioning strategy.

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
Bitrise
Dash
Deque
Google Chrome
JavaScript
Karma
Katalon Recorder
Katalon TestCloud
Node.js
PhantomJS
TestUnity
Testmo

Integrations Supported

Apache Spark
Bitrise
Dash
Deque
Google Chrome
JavaScript
Karma
Katalon Recorder
Katalon TestCloud
Node.js
PhantomJS
TestUnity
Testmo

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

Jasmine

Company Website

jasmine.github.io

Company Facts

Organization Name

Deequ

Company Website

github.com/awslabs/deequ

Categories and Features

Categories and Features

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