Book Image

Learning Apache Spark 2

Book Image

Learning Apache Spark 2

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)

How does it work?


A pipeline is a sequence of stages and each stage is either a Transformer or an Estimator. The stages are run in a sequence in a way that the input frame is transformed as it passes through each stage of the process:

  • Transformer stages: The transform() method on the DataFrame
  • Estimator stages: The fit() method on the DataFrame

A pipeline is created by declaring its stages, configuring appropriate parameters, and then chaining them in a pipeline object. For example, if we were to create a simple classification pipeline we would tokenize the data into columns, use the hashing term feature extractor to extract features, and then build a logistic regression model.

Tip

Please ensure that you add Apache Spark ML Jar either in the class path or build that when you are doing the initial build.

Scala syntax - building a pipeline

This pipeline can be built as follows using the Scala API:

import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.linalg...