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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 Ranger™ serves as a framework designed to facilitate, oversee, and manage extensive data security within the Hadoop ecosystem. The goal of Ranger is to implement a thorough security solution throughout the Apache Hadoop landscape. With the introduction of Apache YARN, the Hadoop platform can effectively accommodate a genuine data lake architecture, allowing businesses to operate various workloads in a multi-tenant setting. As the need for data security in Hadoop evolves, it must adapt to cater to diverse use cases regarding data access, while also offering a centralized framework for the administration of security policies and the oversight of user access. This centralized security management allows for the execution of all security-related tasks via a unified user interface or through REST APIs. Additionally, Ranger provides fine-grained authorization, enabling specific actions or operations with any Hadoop component or tool managed through a central administration tool. It standardizes authorization methods across all Hadoop components and enhances support for various authorization strategies, including role-based access control, thereby ensuring a robust security framework. By doing so, it significantly strengthens the overall security posture of organizations leveraging Hadoop technologies.

Description

MLlib, the machine learning library of Apache Spark, is designed to be highly scalable and integrates effortlessly with Spark's various APIs, accommodating programming languages such as Java, Scala, Python, and R. It provides an extensive range of algorithms and utilities, which encompass classification, regression, clustering, collaborative filtering, and the capabilities to build machine learning pipelines. By harnessing Spark's iterative computation features, MLlib achieves performance improvements that can be as much as 100 times faster than conventional MapReduce methods. Furthermore, it is built to function in a variety of environments, whether on Hadoop, Apache Mesos, Kubernetes, standalone clusters, or within cloud infrastructures, while also being able to access multiple data sources, including HDFS, HBase, and local files. This versatility not only enhances its usability but also establishes MLlib as a powerful tool for executing scalable and efficient machine learning operations in the Apache Spark framework. The combination of speed, flexibility, and a rich set of features renders MLlib an essential resource for data scientists and engineers alike.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Apache HBase Yes 
Apache Hive Yes 
Hadoop Yes 
Amazon EC2 No 
Apache Cassandra No 
Apache Hadoop YARN Yes 
Apache Kafka Yes 
Apache Knox Yes 
Apache Mesos No 
Apache Solr Yes 
Apache Spark No 
Apache Storm Yes 
Java No 
Kubernetes No 
MapReduce No 
PHEMI Health DataLab Yes 
Python No 
R No 
Scala No 

Integrations

Apache HBase Yes 
Apache Hive Yes 
Hadoop Yes 
Amazon EC2 Yes 
Apache Cassandra Yes 
Apache Hadoop YARN No 
Apache Kafka No 
Apache Knox No 
Apache Mesos Yes 
Apache Solr No 
Apache Spark Yes 
Apache Storm No 
Java Yes 
Kubernetes Yes 
MapReduce Yes 
PHEMI Health DataLab No 
Python Yes 
R Yes 
Scala Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version 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 

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

Types of Training

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

Types of Training

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

Vendor Details

Company Name

The Apache Software Foundation

Founded

1999

Country

United States

Website

ranger.apache.org

Vendor Details

Company Name

Apache Software Foundation

Founded

1995

Country

United States

Website

spark.apache.org/mllib/

Product Features

Data Governance

Access Control No 
Data Discovery No 
Data Mapping No 
Data Profiling No 
Deletion Management No 
Email Management No 
Policy Management No 
Process Management No 
Roles Management No 
Storage Management No 

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 

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