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)

Why do we need the Pipeline API?


Before digging into the details of the Pipeline API, it is important to understand what a machine learning pipeline means, and why we need a Pipeline API.

It is important to understand that you cannot have an efficient machine learning platform if the only thing you provide is a bunch of algorithms for people to use. Machine learning is quite an involved process, which involves multiple steps, and a machine learning algorithm itself is just one (though very important) part of the step. As an example, let's consider a text classification example, where you have a corpus of text, and you want to classify if that is a sports article or not a sports article. We would like to simplify it to a 1 and a 0, where a 1 indicates it is about sports and 0 indicates it is not about sports. This is a supervised machine learning flow, where we will use data with existing labels, to predict the labels for data with no labels.

You would need to collect this data. Preprocess...