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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

Apache Mahout is an advanced and adaptable machine learning library that excels in processing distributed datasets efficiently. It encompasses a wide array of algorithms suitable for tasks such as classification, clustering, recommendation, and pattern mining. By integrating seamlessly with the Apache Hadoop ecosystem, Mahout utilizes MapReduce and Spark to facilitate the handling of extensive datasets. This library functions as a distributed linear algebra framework, along with a mathematically expressive Scala domain-specific language, which empowers mathematicians, statisticians, and data scientists to swiftly develop their own algorithms. While Apache Spark is the preferred built-in distributed backend, Mahout also allows for integration with other distributed systems. Matrix computations play a crucial role across numerous scientific and engineering disciplines, especially in machine learning, computer vision, and data analysis. Thus, Apache Mahout is specifically engineered to support large-scale data processing by harnessing the capabilities of both Hadoop and Spark, making it an essential tool for modern data-driven applications.

Description

You determine the cluster size, node specifications, and a range of services, while Yandex Data Proc effortlessly sets up and configures Spark, Hadoop clusters, and additional components. Collaboration is enhanced through the use of Zeppelin notebooks and various web applications via a user interface proxy. You maintain complete control over your cluster with root access for every virtual machine. Moreover, you can install your own software and libraries on active clusters without needing to restart them. Yandex Data Proc employs instance groups to automatically adjust computing resources of compute subclusters in response to CPU usage metrics. Additionally, Data Proc facilitates the creation of managed Hive clusters, which helps minimize the risk of failures and data loss due to metadata issues. This service streamlines the process of constructing ETL pipelines and developing models, as well as managing other iterative operations. Furthermore, the Data Proc operator is natively integrated into Apache Airflow, allowing for seamless orchestration of data workflows. This means that users can leverage the full potential of their data processing capabilities with minimal overhead and maximum efficiency.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Apache Spark Yes 
Hadoop Yes 
Apache Airflow No 
Apache Flume No 
Apache HBase No 
Apache Hive No 
Apache Zeppelin No 
Matplotlib No 
NumPy No 
Python No 
TensorFlow No 
Yandex Cloud No 
Yandex DataSphere No 
pandas No 
scikit-image No 

Integrations

Apache Spark Yes 
Hadoop Yes 
Apache Airflow Yes 
Apache Flume Yes 
Apache HBase Yes 
Apache Hive Yes 
Apache Zeppelin Yes 
Matplotlib Yes 
NumPy Yes 
Python Yes 
TensorFlow Yes 
Yandex Cloud Yes 
Yandex DataSphere Yes 
pandas Yes 
scikit-image Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

$0.19 per hour
Free Trial Yes 
Free Version No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) Yes 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person No 

Vendor Details

Company Name

Apache Software Foundation

Country

United States

Website

mahout.apache.org

Vendor Details

Company Name

Yandex

Founded

1997

Country

Russia

Website

cloud.yandex.com/en/services/data-proc

Product Features

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Product Features

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