Cloudera Developer Training for MapReduce

Course Details
Code:
HADOOP-MAPREDUCE
Tuition (USD):
$3,195.00 • Classroom (4 days)
GSA (USD):
$2,736.27 • Classroom (4 days)

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.

Skills Gained

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

Course Details

Audience and 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.

Developer Certification

Upon completion of the course, attendees are encouraged to continue their study and register for the Cloudera Certified Developer for Apache Hadoop (CCDH) exam. 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.

Introduction

The Motivation For Hadoop

  • Problems with Traditional Large-Scale Systems
  • Introducing Hadoop
  • Hadoopable Problems

Hadoop: Basic Concepts and HDFS

  • The Hadoop Project and Hadoop Components
  • The Hadoop Distributed File System

Introduction to MapReduce

  • MapReduce Overview
  • Example: WordCount
  • Mappers
  • Reducers

Hadoop Clusters and the Hadoop Ecosystem

  • Hadoop Cluster Overview
  • Hadoop Jobs and Tasks
  • Other Hadoop Ecosystem Components

Writing a MapReduce Program in Java

  • Basic MapReduce API Concepts
  • Writing MapReduce Drivers, Mappers, and Reducers in Java
  • Speeding Up Hadoop Development by Using Eclipse
  • Differences Between the Old and New MapReduce APIs

Writing a MapReduce Program Using Streaming

  • Writing Mappers and Reducers with the Streaming API

Unit Testing MapReduce Programs

  • Unit Testing
  • The JUnit and MRUnit Testing Frameworks
  • Writing Unit Tests with MRUnit
  • Running Unit Tests

Delving Deeper into the Hadoop API

  • Using the ToolRunner Class
  • Setting Up and Tearing Down Mappers and reducers
  • Decreasing the Amount of Intermediate
  • Data with Combiners
  • Accessing HDFS Programmatically
  • Using The Distributed Cache
  • Using the Hadoop API’s Library of Mappers, Reducers, and Partitioners

Practical Development Tips and Techniques

  • Strategies for Debugging MapReduce Code
  • Testing MapReduce Code Locally by Using LocalJobRunner
  • Writing and Viewing Log Files
  • Retrieving Job Information with Counters
  • Reusing Objects
  • Creating Map-Only MapReduce Jobs

Partitioners and Reducers

  • How Partitioners and Reducers Work Together
  • Determining the Optimal Number of Reducers for a Job
  • Writing Customer Partitioners

Data Input and Output

  • Creating Custom Writable and Writable
  • Comparable Implementations
  • Saving Binary Data Using SequenceFile and Avro Data Files
  • Issues to Consider When Using File Compression
  • Implementing Custom InputFormats and OutputFormats

Common MapReduce Algorithms

  • Sorting and Searching Large Data Sets
  • Indexing Data
  • Computing Term Frequency — Inverse Document Frequency
  • Calculating Word Co-Occurrence
  • Performing Secondary Sort

Joining Data Sets in MapReduce Jobs

  • Writing a Map-Side Join
  • Writing a Reduce-Side Join

Integrating Hadoop into the Enterprise Workflow

  • Integrating Hadoop into an Existing Enterprise
  • Loading Data from an RDBMS into HDFS by Using Sqoop
  • Managing Real-Time Data Using Flume
  • Accessing HDFS from Legacy Systems with FuseDFS and HttpFS

An Introduction to Hive, Imapala, and Pig

  • The Motivation for Hive, Impala, and Pig
  • Hive Overview
  • Impala Overview
  • Pig Overview
  • Choosing Between Hive, Impala, and Pig

An Introduction to Oozie

  • Introduction to Oozie
  • Creating Oozie Workflows
Course Details
Code:
HADOOP-MAPREDUCE
Tuition (USD):
$3,195.00 • Classroom (4 days)
GSA (USD):
$2,736.27 • Classroom (4 days)