Cloudera Developer Training for Apache Hadoop|System Source

You will learn to build powerful data processing applications in this course. You will learn about MapReduce, the Hadoop Distributed Files System (HDFS), and how to write MapReduce code, and you will cover best practices for Hadoop development, debugging, and implementation of workflows.

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

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Motivation for Hadoop
1. Problems with Traditional Large-Scale Systems
2. Requirements for a New Approach
Hadoop: Basic Concepts
1. Hadoop Distributed File System (HDFS)
2. MapReduce
3. Anatomy of a Hadoop Cluster
4. Other Hadoop Ecosystem Components
Writing a MapReduce Program
1. MapReduce Flow
2. Examining a Sample MapReduce Program
3. Basic MapReduce API Concepts
4. Driver Code
5. Mapper
6. Reducer
7. Streaming API
8. Using Eclipse for Rapid Development
9. New MapReduce API
Integrating Hadoop into the Workflow
1. Relational Database Management Systems
2. Storage Systems
3. Importing Data from a Relational Database Management System with Sqoop
4. Importing Real-Time Data with Flume
5. Accessing HDFS Using FuseDFS and Hoop
Delving Deeper into the Hadoop API
1. ToolRunner
2. Testing with MRUnit
3. Reducing Intermediate Data with Combiners
4. Configuration and Close Methods for Map/Reduce Setup and Teardown
5. Writing Partitioners for Better Load Balancing
6. Directly Accessing HDFS
7. Using the Distributed Cache
Common MapReduce Algorithms
1. Sorting and Searching
2. Indexing
3. Machine Learning with Mahout
4. Term Frequency
5. Inverse Document Frequency
6. Word Co-Occurrence
Using Hive and Pig
1. Hive Basics
2. Pig Basics
Practical Development Tips and Techniques
1. Debugging MapReduce Code
2. Using LocalJobRunner Mode for Easier Debugging
3. Retrieving Job Information with Counters
4. Logging
5. Splittable File Formats
6. Determining the Optimal Number of Reducers
7. Map-Only MapReduce Jobs
Advanced MapReduce Programming
1. Custom Writables and WritableComparables
2. Saving Binary Data Using SequenceFiles and Avro Files
3. Creating InputFormats and OutputFormats
Joining Data Sets in MapReduce
1. Map-Side Joins
2. Secondary Sort
3. Reduce-Side Joins
Graph Manipulation in Hadoop
1. Graph Techniques
2. Representing Graphs in Hadoop
3. Implementing a Sample Algorithm: Single Source Shortest Path
Creating Workflows with Oozie
1. Motivation for Oozie
2. Workflow Definition Format

Recommended: Java OCA & OCP Accelerated
Recommended: Knowledge of Hadoop is not required
Recommended: Some programming experience (preferably Java)

This course covers concepts addressed on the Cloudera Certified Developer for Apache Hadoop (CCDH) exam.

Developers who need to write and maintain Apache Hadoop applications.

Cloudera Developer Training for Apache Hadoop

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