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)

What is Spark Streaming?

Spark Streaming was introduced in Spark 0.7 in early 2013 , with the objective of providing a fault-tolerant scalable architecture that could provide second-scale latency, with a simple programming model and integrated with batch and interactive processing. The industry had given into the idea of having separate platforms for batch and streaming operations, with Storm and Trident being the popular streaming engines of choice in the open source community. Storm would provide at least once semantics while Trident would provide exactly-once semantics. Spark Streaming revolutionized the concept of streaming by allowing users to perform streaming and batching within the same framework and by emphasizing the idea that users should not be worried about the state maintenance of objects. It is now one of the most popular Spark APIs and according to a recent Spark survey carried out by DataBricks, more than 50% of the users consider Spark Streaming as the most important...