Developer Training for Spark and Hadoop


This four-day hands-on training course delivers the key concepts and expertise participants need to ingest and process data on a Hadoop cluster using the most up-to-date tools and techniques. Employing Hadoop ecosystem projects such as Spark, Hive, Flume, Sqoop, and Impala, this training course is the best preparation for the real-world challenges faced by Hadoop developers. Participants will learn to identify which tool is the right one to use in a given situation, and will gain hands-on experience in developing using those tools.





Skills Gained


Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, learning topics such as:




  • How data is distributed, stored, and processed in a Hadoop cluster

  • How to use Sqoop and Flume to ingest data

  • How to process distributed data with Apache Spark

  • How to model structured data as tables in Impala and Hive

  • How to choose the best data storage format for different data usage patterns

  • Best practices for data storage


Course Outline



  • Introduction

  • Introduction to Hadoop and the Hadoop Ecosystem

  • Hadoop Architecture and HDFS

  • Importing Relational Data with Apache Sqoop

  • Introduction to Impala and Hive

  • Modeling and Managing Data with Impala and Hive

  • Data Formats

  • Data Partitioning

  • Capturing Data with Apache Flume

  • Spark Basics

  • Working with RDDs in Spark

  • Writing and Deploying Spark Applications

  • Parallel Programming with Spark

  • Spark Caching and Persistence

  • Common Patterns in Spark Data Processing

  • Spark SQL and DataFrames

  • Conclusion


Audience

Prerequisites


This course is designed for developers and engineers who have programming experience. Apache Spark examples and hands-on exercises are presented in Scala and Python, so the ability to program in one of those languages is required. Basic familiarity with the Linux command line is assumed. Basic knowledge of SQL is helpful. Prior knowledge of Hadoop is not required.