Big Data Processing with Apache Spark

Processing big data in real  time is challenging due to scalability, information consistency, and  fault–tolerance. Big Data Processing with Apache Spark teaches you how  to use Spark to make your overall analytical workflow faster and more  efficient. You’ll explore all core concepts and tools within the Spark  ecosystem, such as Spark Streaming, the Spark Streaming API, machine  learning extension, and structured streaming.

You’ll begin by learning data processing fundamentals using Resilient  Distributed Datasets (RDDs), SQL, Datasets, and Dataframes APIs. After  grasping these fundamentals, you’ll move on to using Spark Streaming  APIs to consume data in real time from TCP sockets, and integrate Amazon  Web Services (AWS) for stream consumption.

By the end of this course, you’ll not only have understood how to use  machine learning extensions and structured streams but you’ll also be  able to apply Spark in your own upcoming big data projects. 

About the Author

Manuel Ignacio Franco  Galeano is a computer scientist from Colombia. He works for Fender  Musical Instruments as a lead engineer in Dublin, Ireland. He holds a  master’s degree in computer science from University College, Dublin UCD.  His areas of interest and research are music information retrieval,  data analytics, distributed systems, and blockchain technologies.

Nimish Narang has graduated from UBC with a degree in biology and  computer science in 2016. He has developed Mobile apps for Android and  iOS since 2015. He is focused on data analysis and machine learning from  the past two years and has previously published Keras and Professional  Scala with Packt.