Big Data Processing using Apache Spark

Course details

Every year we have a big increment of data that we need to store and analyze. When we want to aggregate all data about our users and analyze that data to find insights from it, terabytes of data undergo processing. To be able to process such amounts of data, we need to use a technology that can distribute multiple computations and make them more efficient. Apache Spark is a technology that allows us to process big data leading to faster and scalable processing.

In this course, we will learn how to leverage Apache Spark to be able to process big data quickly. We will cover the basics of Spark API and its architecture in detail. In the second section of the course, we will learn about Data Mining and Data Cleaning, wherein we will look at the Input Data Structure and how Input data is loaded In the third section we will be writing actual jobs that analyze data. By the end of the course, you will have sound understanding of the Spark framework which will help you in writing the code understand the processing of big data.

About the Author

Tomasz Lelek is a Software Engineer, programming mostly in Java, Scala. He is a fan of microservices architecture, and functional programming. He has dedicated considerable time and effort to be better every day. He recently dived into Big Data technologies such as Apache Spark and Hadoop. Tomasz is passionate about nearly everything associated with software development. Recently he was a speaker at conferences in Poland - Confitura and JDD (Java Developers Day) and also at Krakow Scala User Group. He has also conducted a live coding session at Geecon Conference.