MLlibApache Software Foundation
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Related Products
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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.
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About
Apache Spark's MLlib is a scalable machine learning library that integrates seamlessly with Spark's APIs, supporting Java, Scala, Python, and R. It offers a comprehensive suite of algorithms and utilities, including classification, regression, clustering, collaborative filtering, and tools for constructing machine learning pipelines. MLlib's high-quality algorithms leverage Spark's iterative computation capabilities, delivering performance up to 100 times faster than traditional MapReduce implementations. It is designed to operate across diverse environments, running on Hadoop, Apache Mesos, Kubernetes, standalone clusters, or in the cloud, and accessing various data sources such as HDFS, HBase, and local files. This flexibility makes MLlib a robust solution for scalable and efficient machine learning tasks within the Apache Spark ecosystem.
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
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Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
Anyone looking for an Unit Testing solution that measures data quality in large datasets
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Audience
Data scientists and engineers wanting a machine learning solution for efficient data processing and analysis within the Apache Spark framework
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Support
Phone Support
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24/7 Live Support
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Online
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Support
Phone Support
Supported
24/7 Live Support
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Online
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API
Offers API
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API
Offers API
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Pricing
No information available.
Free Version
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Free Trial
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Pricing
No information available.
Free Version
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Free Trial
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
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Live Online
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In Person
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Training
Documentation
Supported
Webinars
Supported
Live Online
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In Person
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Company InformationDeequ
github.com/awslabs/deequ
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Company InformationApache Software Foundation
Founded: 1995
United States
spark.apache.org/mllib/
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Categories |
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Integrations
Apache Spark
Supported
Amazon EC2
Not Supported
Apache Cassandra
Not Supported
Apache HBase
Not Supported
Apache Hive
Not Supported
Apache Mesos
Not Supported
Hadoop
Not Supported
Java
Not Supported
Kubernetes
Not Supported
MapReduce
Not Supported
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Integrations
Apache Spark
Supported
Amazon EC2
Supported
Apache Cassandra
Supported
Apache HBase
Supported
Apache Hive
Supported
Apache Mesos
Supported
Hadoop
Supported
Java
Supported
Kubernetes
Supported
MapReduce
Supported
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