Apache Spark Data Analytics Best Practices & Troubleshooting

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If you face challenges on how to analyze real–time data, create real–world streaming processing in Spark, and face some common pitfalls in your Spark code and are looking for a solution to get you out of the development problems providing you with some best practices so that you can code better, efficiently and faster for analyzing a large amount of data, then this learning series is perfect for you!

With this well thought out Learning Path, you will first begin by learning the fundamentals of Apache Spark which includes Resilient Distributed Datasets (RDD), HDFS, YARN, create effective Spark application and execute it on Hadoop cluster & much more. Then you will learn to analyze data using machine learning techniques and graphs. Moving further you will focus o some amazing tips & tricks to improve particular aspects of programming & administration in Apache Spark & also speed up your Spark jobs by reducing shuffles. Finally, you will learn some quick & simple solutions to troubleshoot development issues and debugging techniques with Apache Spark.

Contents and Overview

This training program includes 4 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Apache Spark Fundamentals you will begin learning about the Apache Spark programming fundamentals such as Resilient Distributed Datasets (RDD) and See which operations can be used to perform a transformation or action operation on the RDD. We’ll show you how to load and save data from various data sources as a different type of files, No–SQL and RDBMS databases, etc.. We’ll also explain Spark advanced programming concepts such as managing Key–Value pairs, accumulators, etc. Finally, you’ll discover how to create an effective Spark application and execute it on the Hadoop cluster to the data and gain insights to make informed business decisions. By the end of this video, you will be well–versed with all the fundamentals of Apache Spark and implementing them in Spark.