Cloudera Developer Training for MapReduce Training

Cloudera Developer Training for MapReduce Training (CDTMR)


Cloudera Developer Training for MapReduce Training Course Description

Cloudera University’s four-day Cloudera Developer Training for MapReduce Training course delivers the key concepts and expertise you will 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 realworld challenges faced by Hadoop developers.

Customize It:

With onsite Training, courses can be scheduled on a date that is convenient for you, and because they can be scheduled at your location, you don’t incur travel costs and students won’t be away from home. Onsite classes can also be tailored to meet your needs. You might shorten a 5-day class into a 3-day class, or combine portions of several related courses into a single course, or have the instructor vary the emphasis of topics depending on your staff’s and site’s requirements.

Audience/Target Group


Cloudera Developer Training for MapReduce Training (CDTMR)Related Courses:

Duration: 4 days

Class Prerequisites:

Knowledge of Java is strongly recommended and is required to complete the hands-on exercises.

What You Will Learn:

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 Content:

Module 1: The Motivation for Hadoop

Problems with Traditional Large-Scale Systems
Introducing Hadoop
Hadoopable Problems

Module 2: Hadoop: Basic Concepts and HDFS

The Hadoop Project and Hadoop Components
The Hadoop Distributed File System

Module 3: Introduction to MapReduce

MapReduce Overview
Example: WordCount

Module 4: Hadoop Clusters and the Hadoop Ecosystem

Hadoop Cluster Overview
Hadoop Jobs and Tasks
Other Hadoop Ecosystem Components

Module 5: 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

Module 6: Writing a MapReduce Program Using Streaming

Writing Mappers and Reducers with the Streaming API

Module 7: Unit Testing MapReduce Programs

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

Module 8: 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

Module 9: 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

Module 10: Partitioners and Reducers

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

Module 11: Data Input and Output

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

Module 12: Common MapReduce Algorithms

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

Module 13: Joining Data Sets in MapReduce Jobs

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

Module 14: 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

Module 15: 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

Module 16: An Introduction to Oozie

Introduction to Oozie
Creating Oozie Workflows

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    Time Frame: 0-3 Months4-12 Months

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