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1. HADOOP COURSE CONTENT
1. THE MOTIVATION OF HADOOP
 Problems with traditional large scale systems
 Requirement for a new apache
 Introducing Hadoop
2. HADOOP BASIC CONCEPTS
 Hadoop project and Hadoop components
 Hadoop distributed file system
 Hadoop on exercise using HDFS
 How map reduce works
 Hands on exercise running a map reduce job
 How a Hadoop cluster operates
 Other Hadoop Ecosystem projects
3. WRITING A MAP REDUCE PROGRAM
 The Map reduce flow
 Basic map reduce API concepts
 Writing map reduce drivers, mappers and reducers in java
 Writing mappers and reducers in another languages using the streaming API
 Speeding up hadoop development by using eclipse
 Hands on exercise writing a Map reduce program
 Difference between old and new Map reduces APIs
4. UNIT TESTING MAP REDUCE PROGRAMS
 Unit testing
 The J unit and MR unit testing frame works
 Writing unit tests and MR units
 Hand on exercise writing unit test and MR test frame works
5. DELVING DEPER IN TO HADOOP API
 Using the tool runner class
 Decreasing the amount of intermediate data with combiners
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2.  Hands on experience writing and implementing combiners
 Setting up and tearing down mappers and reducers by using the configure and
close methods
 Writing custom practitioners for better load balancing
 Hands-on exercise on writing a practitioner
 Accessing HDFS programmatically
 Using the distributed cache
 Using the Hadoop APIs library of mappers, reducers and practitioners
6. PRACTICAL DEVELOPMENT TIPS AND TECHNIQUES
 Strategies for debugging map reduce code
 Testing map reduce code locally by using local job reducer
 Writing and viewing log files
 Retrieving job information with counters
 Determining the optimal number of reducers for a job
 Creating map only map reduce jobs
 Hands on exercise using counters and a map only job
7. DATA INPUT AND OUTPUT
 Creating custom writable and writable comparable implementations
 Saving binary data using sequence file and Avro data files
 Implementing custom input formats and output formats
 Issues to consider when using file compression
 Hands-on exercises using sequence files and file compression
8. COMMAN MAP REDUCE ALLOGORITHMS
 Sorting and searching large data sets
 Performing a secondary sort
 Indexing data
 Hand-on exercise creating an inverted index
 Computing term frequency -inverse document frequency
 Calculating word concurrence
 Hands-on exercise calculating word concurrence
 Hands-on exercise implementing word concurrence with a customer writable
comparable
9. JOINING DATA SETS IN MAP REDUCE JOBS
 Writing a map-side join
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INDIA Trainingicon USA
Phone: +91-966-690-0051 Email: info@trainingicon.com | www.trainingicon.com Phone: +1-408-791-8864

3.  Writing a reduce -side join
10. INTEGRATING HADOOP IN TO ENTERPRISE WORK FLOW
 Integrating hadoop in to an existing enterprise
 Loading data from an RDBMS in to HDFS by using sqoop
 Hands-on exercise importing data with sqoop
 Managing real-time data using flume
 Accessing HDFS from legacy systems with fuse DFS and HTTP FS
11. MACHINE LEARNING AN MAHOUT
 Introduction to machine learning
 using mahout
 Hands-on exercise using a mahout recommended
12. AN INTRODUCTION HIVE AND PIG
 The motivation for HIVE and PIG
 Hive basics
 Hands-on exercise manipulating data with HIVE
 PIG basics
 Hand-on exercise using PIG to retrieve movie names from our recommender
 Choosing between HIVE and PIG
 Introduction to OOZIE,HADOOP ONLINE TRAINING,HADOOP TRAINING
 Creating OOZE work flow
 Hand-on exercise running and OOZE work flow
CONCLUSION
APPENDIX: GRAPH PROCESSING IN MAP REDUCE AN INTRODUCTION TO
OOZIE
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INDIA Trainingicon USA
Phone: +91-966-690-0051 Email: info@trainingicon.com | www.trainingicon.com Phone: +1-408-791-8864