Cloudera Developer Training for MapReduce (CDTAH)

Quantity

$2,995.00

Add to Cart

New Age Technologies has been delivering Authorized Training since 1996. We offer Cloudera’s full suite of authorized courses including courses pertaining to Apache Spark, Hadoop, HBase, MapReduce, Data Science, Cloudera Developer and more. If you have any questions or can’t seem to find the Cloudera class that you are interested in, contact one of our Cloudera Training Specialists. Invest in your future today with Cloudera training from New Age Technologies.

✉ Cloudera Training Specialists | ☏ 502.909.0819

---

Cloudera Developer Training for MapReduce Overview:

The Cloudera Developer Training for MapReduce hands-on course delivers the key concepts and expertise students 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 Developer Training for MapReduce training course is the best preparation for the real world challenges faced by Hadoop developers.

Who Should Attend:

• Developers and engineers who have programming experience

Cloudera Developer Training for MapReduce Prerequisites:

Before attending this course, you must have the following:

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

Cloudera Developer Training for MapReduce Objectives:

After successfully completing this course, you will able to:

• Understand the internals of MapReduce and HDFS and how to write MapReduce code
• Understand best practices for Hadoop development, debugging, and implementation of workflows and common algorithms
• Understand how to leverage Hive, Pig, Sqoop, Flume, Oozie, and other Hadoop ecosystem projects
• Create custom components such as WritableComparables and InputFormats to manage complex data types
• Write and execute joins to link data sets in MapReduce
• Understand advanced Hadoop API topics required for real-world data analysis

Cloudera Developer Training for MapReduce Certification:

• Cloudera Certified Developer for Apache Hadoop (CCDH)

Cloudera Developer Training for MapReduce Outline:

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
• Mappers
• Reducers

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, Impala, 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

---

Average Salary for Skill: MapReduce