Apache Spark for Machine Learning

Answer - D.

The AWS Documentation mentions the following.

Mahout is a machine learning library with tools for clustering, classification, and several types of recommenders, including tools to calculate most-similar items or build item recommendations for users.

Mahout employs the Hadoop framework to distribute calculations across a cluster, and now includes additional work distribution methods, including Spark.

Option A is incorrect since this is used as an open source, non-relational, distributed database developed as part of the Apache Software Foundation's Hadoop project.

Option B is incorrect since this is used for interaction over a REST interface with an EMR cluster running Spark.

Option C is incorrect since this is used as a notebook for interactive data exploration.

For more information on Mahout, please refer to the below URL.

https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-mahout.html

Out of the options given, Livy, Zeppelin, and Mahout can be used on top of Hadoop for machine learning algorithms.

Hadoop is a distributed computing framework that enables the processing of large datasets across a cluster of commodity hardware. It consists of HDFS (Hadoop Distributed File System) for storage and MapReduce for processing. Hadoop is an ideal platform for running machine learning algorithms as it provides a distributed processing environment and can handle large-scale data sets.

Livy is a RESTful web service that enables easy interaction with a Spark cluster. Spark is a fast and general-purpose cluster computing system that provides an API for distributed data processing in Hadoop. Livy provides a way to submit Spark jobs using a REST API, allowing for easy integration with other applications. Livy can be used to submit Spark jobs for machine learning algorithms in Hadoop.

Zeppelin is an open-source web-based notebook that enables interactive data analytics. It provides an environment for data exploration, visualization, and collaboration. Zeppelin supports multiple interpreters, including Spark, Python, R, and SQL. The Spark interpreter allows users to submit Spark jobs for machine learning algorithms.

Mahout is an open-source library that provides scalable machine learning algorithms. Mahout includes implementations of popular machine learning algorithms, such as collaborative filtering, clustering, and classification. Mahout can be run on Hadoop and provides distributed processing for large-scale data sets.

HBase, on the other hand, is a NoSQL database that is built on top of Hadoop. HBase provides random access to large amounts of structured data, but it is not designed for running machine learning algorithms.

In summary, Livy, Zeppelin, and Mahout can be used on top of Hadoop for machine learning algorithms. Livy enables the submission of Spark jobs using a REST API, Zeppelin provides an interactive environment for data analytics, and Mahout provides scalable machine learning algorithms. HBase, on the other hand, is not suitable for running machine learning algorithms.