1. Hadoop Course Contents
(Includes theoretical as well as practical sessions)
Table of Contents
1. Basics of Parallel Programming (4 hours)
a. Multi-Threading
b. OpenMP (Open Multiprocessing)and MPI (Message Passing Interface)
c. Performance tuning and optimization
i. Matrix Multiplication
ii. Unique word count problem
2. Distributed computing concepts (2 hours)
3. Hadoop Overview (6 hours)
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 (8 hours)
a. Details of execution of Map Reduce frame work
b. Word count problem solved using MapReduce programming model.
c. Data Mining on Wikipedia data set.
5. Hadoop ecosystem (2 hours)
6. Hadoop Programming Languages (4 hours)
a. Pig
b. Hadoop Pipes (C++)
c. Hadoop Streaming
d. Hadoop and R
7. Distributed data base concepts (4 hours)
a. RDBMS v/s NoSQL DB
b. Overview of HBase and Cassandra

2. 8. Advance MapReduce 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)