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

Chapter 12. Common Recipes for Implementing a Robust Machine Learning System

In this chapter, we will cover:

  • Spark's basic statistical API to help you build your own algorithms
  • ML pipelines for real-life machine learning applications
  • Normalizing data with Spark
  • Splitting data for training and testing
  • Common operations with the new Dataset API
  • Creating and using RDD versus DataFrame versus Dataset from a text file in Spark 2.0
  • LabeledPoint data structure for Spark ML
  • Getting access to Spark cluster in Spark 2.0+
  • Getting access to Spark cluster pre-Spark 2.0
  • Getting access to SparkContext vis-a-vis SparkSession object in Spark 2.0
  • New model export and PMML markup in Spark 2.0
  • Regression model evaluation using Spark 2.0
  • Binary classification model evaluation using Spark 2.0
  • Multilabel classification model evaluation using Spark 2.0
  • Multiclass classification model evaluation using Spark 2.0
  • Using the Scala Breeze library to do graphics in Spark 2.0