MLlibApache Software Foundation
|
ZetaAnalyticsHalliburton
|
|||||
Related Products
|
||||||
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.
|
About
The ZetaAnalytics product requires a compatible database appliance for its Data Warehouse. Landmark has qualified the ZetaAnalytics software using Teradata, EMC Greenplum, and IBM Netezza. Please see the ZetaAnalytics Release Notes for the most up to date qualified versions. Before installing and configuring ZetaAnalytics software, ensure that the Data Warehouse you use for drilling data is created and running. Scripts to create the various Zeta-specific database components within the Data Warehouse will need to be run as part of the installation process. These require database administrator (DBA) rights. The ZetaAnalytics product requires Apache Hadoop for model scoring and real-time streaming. If you do not already have an Apache Hadoop cluster installed in your environment, please install it before running the ZetaAnalytics installer, which will prompt you for the name and port number of your Hadoop Name Server and Map Reducer.
|
|||||
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
|
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
|
|||||
Audience
Data scientists and engineers wanting a machine learning solution for efficient data processing and analysis within the Apache Spark framework
|
Audience
Oil and gas companies and other organizations looking for a software solution to streamline their operational needs
|
|||||
Support
Phone Support
Supported
24/7 Live Support
Not Supported
Online
Supported
|
Support
Phone Support
Not Supported
24/7 Live Support
Supported
Online
Supported
|
|||||
API
Offers API
Supported
|
API
Offers API
Not Supported
|
|||||
Screenshots and Videos |
Screenshots and Videos |
|||||
Pricing
No information available.
Free Version
Not Supported
Free Trial
Not Supported
|
Pricing
No information available.
Free Version
Not Supported
Free Trial
Not Supported
|
|||||
Reviews/
|
Reviews/
|
|||||
Training
Documentation
Supported
Webinars
Supported
Live Online
Not Supported
In Person
Supported
|
Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Supported
|
|||||
Company InformationApache Software Foundation
Founded: 1995
United States
spark.apache.org/mllib/
|
Company InformationHalliburton
www.halliburton.com
|
|||||
Alternatives |
Alternatives |
|||||
|
|
|
|||||
|
|
|
|||||
|
|
|
|||||
|
|
||||||
Categories |
Categories |
|||||
Integrations
Amazon EC2
Supported
Apache Cassandra
Supported
Apache HBase
Supported
Apache Hive
Supported
Apache Mesos
Supported
Apache Spark
Supported
Hadoop
Supported
Java
Supported
Kubernetes
Supported
MapReduce
Supported
|
Integrations
Amazon EC2
Not Supported
Apache Cassandra
Not Supported
Apache HBase
Not Supported
Apache Hive
Not Supported
Apache Mesos
Not Supported
Apache Spark
Not Supported
Hadoop
Not Supported
Java
Not Supported
Kubernetes
Not Supported
MapReduce
Not Supported
|
|||||
|
|
|