Book Image

Learning Apache Spark 2

By : Abbasi
Book Image

Learning Apache Spark 2

By: Abbasi

Overview of this book

Apache Spark has seen an unprecedented growth in terms of its adoption over the last few years, mainly because of its speed, diversity and real-time data processing capabilities. It has quickly become the preferred choice of tool for many Big Data professionals looking to find quick insights from large chunks of data. This book introduces you to the Apache Spark framework, and familiarizes you with all the latest features and capabilities introduced in Spark 2. Starting with a detailed introduction to Spark’s architecture and the installation procedure, this book covers everything you need to know about the Spark framework in the most practical manner. You will learn how to perform the basic ETL activities using Spark, and work with different components of Spark such as Spark SQL, as well as the Dataset and DataFrame APIs for manipulating your data. Then, you will perform machine learning using Spark MLlib, as well as perform streaming analytics and graph processing using the Spark Streaming and GraphX modules respectively. The book also gives special emphasis on deploying your Spark models, and how they can be operated in a clustered mode. During the course of the book, you will come across implementations of different real-world use-cases and examples, giving you the hands-on knowledge you need to use Apache Spark in the best possible manner.
Table of Contents (12 chapters)

Introduction to Spark MLLib


MLLib stands for Machine Learning Library in Spark and is designed to make machine learning scalable, approachable, and easy for data scientists and engineers. It was created in the Berkley AMPLab and shipped with Spark 0.8.

Spark MLLib is a very active project with huge contributions from the community and an ever growing coverage of machine learning algorithms in the areas of classification, regression, clustering, recommendation, and other utilities such as feature extraction, feature selection, summary statistics, linear algebra, and frequent pattern matching.

Version 0.8 started small with the introduction of limited algorithms, such as:

  • KMeans
  • Alternating Least Squares (ALS)
  • Gradient Descent (Optimization Technique)

From an API perspective, support for these algorithms was made available in the following programs:

  • Java
  • Scala

The amazing pace of MLLib can be gauged from the fact that within 3 months, version 0.9 was launched, which added the following...