HADOOP COURSE CONTENT (Includes theoretical as well as practical sessions) Table of Contents- With Real Time Faculty 1. Basics of Parallel Programming a. Multi-Threading b. Open MP (Open Multiprocessing)and MPI (Message Passing Interface) c. Performance tuning and optimization i. Matrix Multiplication ii. Unique word count problem 2. Distributed computing concepts 3. Hadoop Overview a. Why Hadoop? b. Brief history of hadoop c. Architecture of Hadoop d. Overview of HDFS (Hadoop Distributed File System) and MR (Map Reduce) framework e. Overview of problems solved by Hadoop i. Data Mining ii. Web Mining iii. Natural Language Processing iv. K-means clustering v. Sentimental Analysis 4. Map Reduce Programming Model a. Details of execution of Map Reduce frame work b. Word count problem solved using Map Reduce programming model. c. Data Mining on Wikipedia data set. 5. Hadoop ecosystem 6. Hadoop Programming Languages a. Pig b. Hadoop Pipes (C++) c. Hadoop Streaming d. Hadoop and R 7. Distributed data base concepts a. RDBMS v/s No SQL DB b. Overview of HBase and Cassandra 8. Advance Map Reduce Programming (chaining Mapper and Reducer) 9. Case Studies A. Data Mining on Wikipedia data set using a. Batch Mode Processing (MR ) b. Using Hive c. Using HBase and Hive B. Web Mining using Apache Nutch, Apache Solr and Hadoop C. Web Log processing using Flume and Hadoop D. Complex Event processing using Flume, Hadoop and EPL ( Event Processing Language) E. Integrating Hadoop and RDBMS Prerequisites: (1) Hands-on Core java programming / C++/ R/Python (2) Hands on parallel/multithreaded programming (3) Query Language (SQL or EPL) (Optional)

1. HADOOP ONLINE TRAINING
COURSE CONTENT
Duration: Approximately 40 days

2. HADOOP
 what is Hadoop
 Why Hadoop and its usecases (Demo Part)
 Different components of Hadoop
HDFS(Hadoop Distributed File System)
 Significance of HDFS in Hadoop
 Features of Hadoop
5 Deamons of Hadoop
 NameNode and its functionality
 DataNode and its functionality
 JobTracker and its functionality
 TaskTracker and its functionality
 Secondary Name Node and its functionality
Datastorage in HDFS
 Introduction about Blocks
 Data Replication
Accessing HDFS
 CLI(Command line Interface)
 Java based approach
 Hadoop Archives
Map Reduce
 Map Reduce Architecture
 Map Reduce Programming Model
 Different phases of Map reduce Algorithm
 Different Data Types in Map Reduce
How to write a basic Map Reduce program
 The Driver code
 The Mapper
 The Reducer
 Joining data sets in MapReduce jobs – Map Joins and Reduce Joins
Creating Input and Output formats in Map Reduce jobs
 Text Input Format
 Key-Value Input Format
 Sequence File Input Format
How to debug MapReduce Jobs in Local and Pseudo cluster mode
 Intoduction to Mapreduce Streaming and pipelining
 Data Localization in map reduce
 Combiner(mini reducer) and Partitioner
 Distributed Cache
 Secondary Sorting Using Map Reduce
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info@sandsys.net

3. SANDSYS
TECHNOLOGIES
APACHE PIG
 Introduction to Apache PIG
 Map Reduce Vs Apache PIG
 SQL Vs Apache PIG
 Different Data Types in PIG
 Modes of Execution in PIG
 Local Mode
 Map Reduce OR Distributed Mode
 Execution Mechanism
 Grunt Shell
 Script
 Embedded
 Transformations in PIG
How to write a simple pig script
 UDFs in PIG
HIVE
 HIVE Introduction
 Hive Architecture
 Hive Meta Store
 Hive Integration with Hadoop
 Hive Query language(HQL)
 SQL Vs HQL
 Hive UDF
 Hive UDAF
 Hive SerDe
 Hive Transformations
SQOOP
 Introduction to SQOOP
 How to connect to Relational Database using SQOOP
 Different SQOOP commands
 Different flavours of Imports
 Export
 Hive –Imports
Oozie Flume
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