Book Image

Apache Spark 2: Data Processing and Real-Time Analytics

By : Romeo Kienzler, Md. Rezaul Karim, Sridhar Alla, Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen Mei
Book Image

Apache Spark 2: Data Processing and Real-Time Analytics

By: Romeo Kienzler, Md. Rezaul Karim, Sridhar Alla, Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen Mei

Overview of this book

Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark's functionality and building your own data flow and machine learning programs on this platform. You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools. By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle. This Learning Path includes content from the following Packt products: • Mastering Apache Spark 2.x by Romeo Kienzler • Scala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar Alla • Apache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbook
Table of Contents (23 chapters)
Title Page
Copyright
About Packt
Contributors
Preface
Index

Loading DataFrames and setup from an external source


In this recipe, we examine data manipulation using SQL. Spark's approach to provide, both a pragmatic and SQL interface works very well in production settings in which we not only require machine learning, but also access to existing data sources using SQL to ensure compatibility and familiarity with existing SQL-based systems. DataFrame with SQL makes for an elegant process toward integration in real-life settings.

How to do it...

  1. Start a new project in IntelliJ or in an IDE of your choice. Make sure the necessary JAR files are included.
  1. Set up the package location where the program will reside:
package spark.ml.cookbook.chapter3
  1. Set up the imports related to DataFrame and the required data structures and create the RDDs as needed for the example:
import org.apache.spark.sql._
  1. Import the packages for setting up the logging level forlog4j. This step is optional, but we highly recommend it (change the level appropriately as you move through the...