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Description

Catch2 serves primarily as a unit testing framework tailored for C++, yet it also incorporates fundamental micro-benchmarking capabilities and straightforward BDD macros. Its primary strength lies in its user-friendly and intuitive design. Test identifiers do not require adherence to valid naming conventions, assertions resemble standard C++ boolean expressions, and the use of sections allows for a localized approach to managing setup and teardown code within tests. Currently, you are working on the devel branch where version 3 is under development. This upcoming version introduces several major updates, the most notable being that Catch2 transitions from a single-header library to a conventional library structure featuring multiple headers and a separately compiled implementation. Getting started is quick and straightforward; you only need to download two files, integrate them into your project, and you're ready to go, all without any external dependencies. As long as your environment supports C++14 and includes the C++ standard library, you can write test cases as self-registering functions or methods if that suits your style. This flexibility in coding approaches enhances the framework's usability for various programming preferences.

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.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Apache Spark No 
C++ Yes 
Codecov Yes 

Integrations

Apache Spark Yes 
C++ No 
Codecov No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Catch2

Website

github.com/catchorg/Catch2

Vendor Details

Company Name

Deequ

Website

github.com/awslabs/deequ

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