+
+

Related Products

  • SenseIP
    1 Rating
    Visit Website
  • Vertex AI
    783 Ratings
    Visit Website
  • Google Cloud Platform
    60,448 Ratings
    Visit Website
  • Parasoft
    137 Ratings
    Visit Website
  • Gearset
    228 Ratings
    Visit Website
  • qTest
    Visit Website
  • dbt
    219 Ratings
    Visit Website
  • RunMyJobs by Redwood
    246 Ratings
    Visit Website
  • Bitrise
    389 Ratings
    Visit Website
  • Dragonfly
    16 Ratings
    Visit Website

About

Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets. We are happy to receive feedback and contributions. Deequ depends on Java 8. Deequ version 2.x only runs with Spark 3.1, and vice versa. If you rely on a previous Spark version, please use a Deequ 1.x version (legacy version is maintained in legacy-spark-3.0 branch). We provide legacy releases compatible with Apache Spark versions 2.2.x to 3.0.x. The Spark 2.2.x and 2.3.x releases depend on Scala 2.11 and the Spark 2.4.x, 3.0.x, and 3.1.x releases depend on Scala 2.12. Deequ's purpose is to "unit-test" data to find errors early, before the data gets fed to consuming systems or machine learning algorithms. In the following, we will walk you through a toy example to showcase the most basic usage of our library.

About

PySpark is an interface for Apache Spark in Python. It not only allows you to write Spark applications using Python APIs, but also provides the PySpark shell for interactively analyzing your data in a distributed environment. PySpark supports most of Spark’s features such as Spark SQL, DataFrame, Streaming, MLlib (Machine Learning) and Spark Core. Spark SQL is a Spark module for structured data processing. It provides a programming abstraction called DataFrame and can also act as distributed SQL query engine. Running on top of Spark, the streaming feature in Apache Spark enables powerful interactive and analytical applications across both streaming and historical data, while inheriting Spark’s ease of use and fault tolerance characteristics.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Anyone looking for an Unit Testing solution that measures data quality in large datasets

Audience

Application development solution for DevOps teams

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Deequ
github.com/awslabs/deequ

Company Information

PySpark
spark.apache.org/docs/latest/api/python/

Alternatives

Spark Streaming

Spark Streaming

Apache Software Foundation

Alternatives

Apache Spark

Apache Spark

Apache Software Foundation
MLlib

MLlib

Apache Software Foundation
Apache Spark

Apache Spark

Apache Software Foundation
Apache Mahout

Apache Mahout

Apache Software Foundation
Spark Streaming

Spark Streaming

Apache Software Foundation

Categories

Categories

Integrations

Apache Spark
Amazon SageMaker Data Wrangler
Comet LLM
Feast
Fosfor Decision Cloud
Tecton
Union Pandera

Integrations

Apache Spark
Amazon SageMaker Data Wrangler
Comet LLM
Feast
Fosfor Decision Cloud
Tecton
Union Pandera
Claim Deequ and update features and information
Claim Deequ and update features and information
Claim PySpark and update features and information
Claim PySpark and update features and information