Cloudera Developer Training for Apache Hadoop

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Cloudera Developer Training for Apache Hadoop

HDP-103

Cloudera University’s four-day developer training course delivers the key concepts and expertise participants need to create robust data processing applications using Apache Hadoop. From workflow implementation and working with APIs through writing MapReduce code and executing joins, Cloudera’s training course is the best preparation for the real-world challenges faced by Hadoop developers.

Course Objectives

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

  • The internals of MapReduce and HDFS and how to write MapReduce code
  • Best practices for Hadoop development, debugging, and implementation of workflows and common algorithms
    How to leverage Hive, Pig, Sqoop, Flume, Oozie, and other Hadoop ecosystem projects
  • Creating custom components such as WritableComparables and InputFormats to manage complex data types
  • Writing and executing joins to link data sets  in MapReduce
  • Advanced Hadoop API topics required for real-world data analysis
Audience & Prerequisites

This course is best suited to developers and engineers who have programming experience. Knowledge of Java is strongly recommended and is required to complete the hands-on exercises.

CCDH Certification

Upon completion of the course, attendees receive a Cloudera Certified Developer for Apache Hadoop (CCDH) practice test. Certification is a great differentiator; it helps establish  you as a leader in the field, providing employers and customers with tangible evidence of your skills and expertise.

Course Topics
  • The Motivation for Hadoop
  • Hadoop: Basic Concepts and HDFS
  • Introduction to MapReduce
  • Hadoop Clusters and the Hadoop Ecosystem
  • Writing a MapReduce Program in Java
  • Writing a MapReduce Program Using Streaming
  • Unit Testing MapReduce Programs
  • Delving Deeper into the Hadoop API
  • Practical Development Tips and Techniques
  • Partitioners and Reducers
  • Data Input and Output
  • Common MapReduce Algorithms
  • Joining Data Sets in MapReduce Jobs
  • Integrating Hadoop into the Enterprise Workflow
  • An Introduction to Hive, Imapala, and Pig
  • An Introduction to Oozie
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