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 11. Spark's Three Data Musketeers for Machine Learning - Perfect Together

In this chapter, we will cover the following recipes:

  • Creating RDDs with Spark 2.0 using internal data sources
  • Creating RDDs with Spark 2.0 using external data sources
  • Transforming RDDs with Spark 2.0 using the filter() API
  • Transforming RDDs with the super useful flatMap() API
  • Transforming RDDs with set operation APIs
  • RDD transformation/aggregation with groupBy() and reduceByKey()
  • Transforming RDDs with the zip() API
  • Join transformation with paired key-value RDDs
  • Reduce and grouping transformation with paired key-value RDDs
  • Creating DataFrames from Scala data structures
  • Operating on DataFrames programmatically without SQL
  • Loading DataFrames and setup from an external source
  • Using DataFrames with standard SQL language - SparkSQL
  • Working with the Dataset API using a Scala sequence
  • Creating and using Datasets from RDDs and back again
  • Working with JSON using the Dataset API and SQL together
  • Functional programming with the Dataset...