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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

IBM Analytics Engine provides an architecture for Hadoop clusters that decouples the compute and storage tiers. Instead of a permanent cluster formed of dual-purpose nodes, the Analytics Engine allows users to store data in an object storage layer such as IBM Cloud Object Storage and spins up clusters of computing notes when needed. Separating compute from storage helps to transform the flexibility, scalability and maintainability of big data analytics platforms. Build on an ODPi compliant stack with pioneering data science tools with the broader Apache Hadoop and Apache Spark ecosystem. Define clusters based on your application's requirements. Choose the appropriate software pack, version, and size of the cluster. Use as long as required and delete as soon as an application finishes jobs. Configure clusters with third-party analytics libraries and packages. Deploy workloads from IBM Cloud services like machine learning.

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

Enterprises in need of an architecture for Hadoop clusters to decouples the compute and storage tiers

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

$0.014 per hour
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

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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

IBM
Founded: 1911
United States
www.ibm.com/cloud/analytics-engine

Alternatives

Spark Streaming

Spark Streaming

Apache Software Foundation

Alternatives

E-MapReduce

E-MapReduce

Alibaba
Hadoop

Hadoop

Apache Software Foundation
Apache Spark

Apache Spark

Apache Software Foundation
MLlib

MLlib

Apache Software Foundation
Apache Sentry

Apache Sentry

Apache Software Foundation
Apache Mahout

Apache Mahout

Apache Software Foundation
Azure HDInsight

Azure HDInsight

Microsoft

Categories

Categories

Integrations

Apache Spark
Acquia CDP
Galileo
Hadoop
IBM Cloud Object Storage
MINT
RadiantOne
Switch Automation
ZARUS

Integrations

Apache Spark
Acquia CDP
Galileo
Hadoop
IBM Cloud Object Storage
MINT
RadiantOne
Switch Automation
ZARUS
Claim Deequ and update features and information
Claim Deequ and update features and information
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Claim IBM Analytics Engine and update features and information