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