Professional Hadoop Development

Takes a participant from no knowledge of Hadoop to being able to develop with Hadoop professionally. It covers the main technologies of Hadoop: HDFS and MapReduce. There is an in-depth coverage of essential Big Data and Hadoop ecosystem technologies. The class ends with a consideration of how to architect Big Data solutions with Hadoop and its ecosystem.

Duration: 4 days

Intended Audience: Technical, Software Engineers, QA, Analysts

Prerequisites: Intermediate-Level Java

You Will Learn

  • What exists in the Big Data ecosystem so you can use the right tool for the right job.
  • An understanding of how HDFS works and how to interact with it.
  • An understanding of how MapReduce works and how each phase works.
  • The basics of coding a MapReduce job with Java to build your Big Data foundation.
  • What the advanced features of the MapReduce API that only the true experts know.
  • How Apache Crunch gives you a very different API from MapReduce and gives you a more Java-centric API.
  • How to use Apache Crunch to do the things not humanly possible in MapReduce like joining datasets and performing secondary sorts.
  • The simple and advanced SQL-like commands available in Hive.
  • How to extend Hive commands with custom non-Java code to do company or use case specific queries.
  • How to move data out of and into relational databases like MySQL and Oracle from Hadoop/Spark using Apache Sqoop.
  • How to move files and network data from many different computers to Hadoop using Apache Flume.
  • What is Hue and how it aids in creating browser-based data products.
  • How Apache Oozie makes it possible to create repeatable workflows that enterprises need.
  • How all of these technologies come together as a solution for ETL, click stream, and sessionization use cases.
  • The steps and iterations to take when creating a Big Data solution. 

Course Outline

Professional Hadoop Development
Thinking in Big Data
  Introducing Big Data
  What is Hadoop?
  The Ecosystem
  Introduction to HDFS
  Introduction to MapReduce
Coding with MapReduce
  Java API
  Streaming API
  Using Eclipse
  Regular Expressions
  Using Apache Maven
Advanced MapReduce
  Advanced MapReduce Classes
  Unit Testing
  Avro
  MapReduce and Avro
Coding With Crunch
  Using Crunch
  Crunch API Pipelines
Advanced Crunch
  Joins
  Crunch Operations
  Secondary Sorts
  Unit Testing
Using Hive
  Hive Overview
  Hive Queries
  Advanced Queries
Augmenting Hive With UDFs and Transforms
  Hive Transforms
  Hive UDFs
Pig Overview
  Pig
Moving and Accessing Data
  Sqoop
  Flume
Creating Workflows
  Hue
  Oozie
  Hue and Oozie
Hadoop Architectures
  ETL
  Click Steam
  Other Architectures
Conclusion

Technologies Covered

  • Apache Hadoop
  • Apache Spark
  • Apache Hive
  • Apache Pig
  • Apache HBase
  • Apache Impala
  • Apache Kafka
  • Apache Crunch
  • Hue
  • Apache Oozie

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