Hadoop Training CourseTraining Courses
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- Hadoop Course Introduction
Hadoop is an Apache open source framework written in java that allows distributed processing of large datasets across clusters of computers using simple programming models. A Hadoop frame-worked application works in an environment that provides distributed storage and computation across clusters of computers. Hadoop is designed to scale up from single server to thousands of machines, each offering local computation and storage. - Hadoop framework includes following four modules:
- Hadoop Common: These are Java libraries and utilities required by other Hadoop modules.
- These libraries provides filesystem and OS level abstractions and contains the necessary Java files and scripts required to start Hadoop.
- Hadoop YARN: This is a framework for job scheduling and cluster resource management.
- Hadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput access to application data.
- Hadoop MapReduce: This is YARN-based system for parallel processing of large data sets.
Hadoop: Basic Concepts
• An Overview of Hadoop
• The Hadoop Distributed File System
• Hands-On Exercise
• How MapReduce Works
• Hands-On Exercise
• Anatomy of a Hadoop Cluster
• Other Hadoop Ecosystem Components
Writing a MapReduce Program
• The MapReduce Flow
• Examining a Sample MapReduce Program
• Basic MapReduce API Concepts
• The Driver Code
• The Mapper
• The Reducer
• Hadoop’s Streaming API
• Using Eclipse for Rapid Development
• Hands-on exercise
• The New MapReduce API
Common MapReduce Algorithms
• Sorting and Searching
• Indexing
• Machine Learning With Mahout
• Term Frequency – Inverse Document Frequency
• Word Co-Occurrence
• Hands-On Exercise.
PIG
• Data loading in PIG.
• Data Extraction in PIG.
• Data Transformation in PIG.
• Hands on exercise on PIG.
Hive
• Hive Query Language.
• Alter and Delete in Hive.
• Partition in Hive.
• Indexing.
• Joins in Hive.Unions in hive.
• Industry specific configuration of hive parameters.
• Authentication & Authorization.
• Statistics with Hive.
• Archiving in Hive.
• Hands-on exercise
Working with Sqoop
• Introduction.
• Import Data.
• Export Data.
• Sqoop Syntaxs.
• Databases connection.
• Hands-on exercise
Working with Flume
• Introduction.
• Configuration and Setup.
• Flume Sink with example.
• Channel.
• Flume Source with example.
• Complex flume architecture.
OOZIE Concepts
IMPALA Concepts
HUE Concepts
HBASE Concepts
ZooKeeper concepts
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