HADOOP training in BTM at RAKS Technologies - Bangalore

Course Prerequisites:
Basic knowledge in some programming language (Java, python,…) and unix operating system.
Content:
Introduction
○ Grid Computing
○ Virtualization
○ Cloud computing
○ Cloud service providers (Amazon, NetApp, Dell, EMC, IBM, Oracle… etc)
○ Comparison with RDBMS
○ Data Retrieval – Random Access Vs. Sequential Access
○ No SQL Databases.
The Hadoop Motivation
○ Problems with traditional large-scale systems
○ Increase in data sizes
○ Difficulties in storing web data into RDBMS
Hadoop: Basic concepts
○ What is Hadoop?
○ The Hadoop Distributed File System (HDFS)
○ The MapReduce Paradigm
Hadoop Installation
○ Preparing unix machine for Hadoop setup
○ Basic configuration parameters
○ Checking health of the cluster
○ Different processes in Hadoop and their significance
HDFS
○ Importance (Reliability, High availability,…)
○ Command line shell usage
○ Using Distcp
○ Using WebHDFS
MapReduce Paradigm
○ Word count program
○ Deeper look into the word count code
○ Using Streaming API
Advanced Features in MapReduce
○ Combiners
○ Partitioners
○ Tool Runner
○ Distributed Cache
○ Custom Writable and WritableComparables
○ Creating InputFormats and OutputFormats
○ ChainMapper and ChainReducer
○ MultipleInputs and MultipleOutputs
○ JobControl
○ Mapside Join and ReduceSide Join
○ Handling small files problems in Hadoop
○ Schedulers
Sample MapReduce Codes
○ Sorting and Searching
○ Grep
○ Document wise word count
○ Secondary Sort
Debugging & Testing
○ Running MapReduce in Local and Pseudo-distributed modes
○ Testing MapReduce with MRUnit
○ Using Counters
○ Logging
Performance tuning
○ Traffic reduce over the network using Combiners
○ Using Compression
○ Using Partitioners in right place
Other Apache tools
○ Sqoop
○ Hive
○ Pig