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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

Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads. Data lakes typically have multiple data pipelines reading and writing data concurrently, and data engineers have to go through a tedious process to ensure data integrity, due to the lack of transactions. Delta Lake brings ACID transactions to your data lakes. It provides serializability, the strongest level of isolation level. Learn more at Diving into Delta Lake: Unpacking the Transaction Log. In big data, even the metadata itself can be "big data". Delta Lake treats metadata just like data, leveraging Spark's distributed processing power to handle all its metadata. As a result, Delta Lake can handle petabyte-scale tables with billions of partitions and files at ease. Delta Lake provides snapshots of data enabling developers to access and revert to earlier versions of data for audits, rollbacks or to reproduce experiments.

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

Companies looking for a storage layer software solution for big data workloads

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

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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

Delta Lake
Founded: 2019
United States
delta.io

Alternatives

Spark Streaming

Spark Streaming

Apache Software Foundation

Alternatives

Apache Hudi

Apache Hudi

Apache Corporation
Apache Iceberg

Apache Iceberg

Apache Software Foundation
Apache Spark

Apache Spark

Apache Software Foundation
Apache Kudu

Apache Kudu

The Apache Software Foundation
MLlib

MLlib

Apache Software Foundation
Apache Mahout

Apache Mahout

Apache Software Foundation

Categories

Categories

Integrations

Apache Spark
Alibaba Cloud
Amundsen
Arroyo
Blue Planet
Booz Allen MDR
DataHub
IBM StreamSets
Kyvos Semantic Layer
McGraw-Hill Connect
MySQL
Okera
Onehouse
OpenMetadata
Secoda
Split
Subsalt
Tableau
TencentDB
Upwork

Integrations

Apache Spark
Alibaba Cloud
Amundsen
Arroyo
Blue Planet
Booz Allen MDR
DataHub
IBM StreamSets
Kyvos Semantic Layer
McGraw-Hill Connect
MySQL
Okera
Onehouse
OpenMetadata
Secoda
Split
Subsalt
Tableau
TencentDB
Upwork
Claim Deequ and update features and information
Claim Deequ and update features and information
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Claim Delta Lake and update features and information