MindScripts is the best Big-Data Hadoop Training Institute/Center in Pune providing complete courses including Cloudera, Hortonworks, HDFS, MapReduce, Pig, Hive, Sqoop, ZooKeeper. The course is designed keeping CloudEra Certification syllabus in mind.

1. Hadoop Training Institutes in Pune
Introduction:Grab the Excellent & Huge Job opportunities in the Big-Data Hadoop Market today with the help of
Mindscripts Technologies. Mindscripts Technologies is the Best Big-Data Hadoop Training Institutes in
Pune. Providing you a Complete Hadoop Course with Cloud Era Certification. As Hadoop Course is
developed & work under Apache License. Big-data Hadoop is the Latest Technology in IT Market. The
Main objective of this course is to provide the complete knowledge to the candidates or to the Cocorporate employee who are interested to make their career in Big-Data Hadoop course. Target & final
Career objective is to complete the Hadoop course from mindscripts technologies, is to complete the
Course & get the Job in Multinational companies for starting your career.
For More Details you may Contact us on:-

2. Hotline no - 9595957557
Mobile no – 8805674210
Website:- www.mindscripts.com
Office Branch Address:MindScripts Technologies,
2nd Floor, Siddharth Hall,
Near Ranka Jewellers,
Behind HP Petrol Pump,
Karve Rd,
Pune 411004
MindScripts Technologies,
C8, 2nd Floor, Sant Tukaram Complex ,
Pradhikaran, Above Savali Hotel,
Opp Nigdi Bus Stand,
Nigdi,
Pune – 411044
Objective:•
Get Complete Knowledge Of Big-Data with Live Projects at the end of the Course.
•
Complete Big-Data Hadoop Course with CloudEra Certification from Technical Experts.
Eligibility:•
Candidate should a Graduate before applying for this course.
•
He should have the knowledge of Java. And Java skills are mandatory or he should posses a past
Experience in Software Development field.
Course Overview:The Course consists of following modules which include both theoretical & Practical knowledge.
Hadoop
Hadoop Ecosystem
Introduction
Hadoop: Basic Concepts
What is Hadoop?
The Hadoop Distributed File System
Hadoop Map Reduce Works
HBase
HBase concepts
HBase architecture
Region server architecture
File storage architecture
HBase basics

3. Anatomy of a Hadoop Cluster
Column access
Scans
Hadoop demons
HBase use cases
Master Daemons
Install and configure HBase on a multi node
Name node
cluster
Job Tracker
Create database, Develop and run sample
Secondary name node
applications
Slave Daemons
Access data stored in HBase using clients like
Job tracker
Java, Python and Pearl
Task tracker
HBase and Hive Integration
HBase admin tasks
HDFS ( Hadoop Distributed File System )
Defining Schema and basic operation
Blocks and Splits
Hive
Input Splits
Hive concepts
HDFS Splits
Hive architecture
Data Replication
Install and configure hive on cluster
Hadoop Rack Aware
Create database, access it from java client
Data high availability
Buckets
Data Integrity
Partitions
Cluster architecture and block placement
Joins in hive
Accessing HDFS
Inner joins
JAVA Approach
Outer Joins
CLI Approach
Hive UDF
Programming Practices
Hive UDAF
Developing MapReduce Programs in
Hive UDTF
Local Mode
Develop and run sample applications in
Running without HDFS and Mapreduce Java/Python to access hive
Pseudo-distributed Mode
Running all daemons in a single node
PIG
Fully distributed mode
Pig basics
Running daemons on dedicated nodes
Install and configure PIG on a cluster
PIG Vs MapReduce and SQL
Setup Hadoop cluster
Pig Vs Hive
Make a fully distributed Hadoop cluster
Write sample Pig Latin scripts
Cluster Specification
Modes of running PIG
Network Topology
Running in Grunt shell
Cluster Specification and installation
Programming in Eclipse
Hadoop configuration
Running as Java program
PIG UDFs
Writing a MapReduce Program
Pig Macros
Examining a Sample MapReduce Program
With several examples
Flume
Basic API Concepts
Flume concepts
The Driver Code
Install and configure flume on cluster
The Mapper
Create a sample application to capture logs
The Reducer
from Apache using flume

4. Hadoop's Streaming API
Performing several hadoop jobs
The configure and close Methods
Sequence Files
Record Reader
Record Writer
Role of Reporter
Output Collector
Processing XML files
Counters
Directly Accessing HDFS
ToolRunner
Using The Distributed Cache
Common MapReduce Algorithms
Sorting and Searching
Indexing
Classification/Machine Learning
Term Frequency - Inverse Document
Frequency
Word Co-Occurrence
Hands-On Exercise: Creating an Inverted
Index
Identity Mapper
Identity Reducer
Exploring well known problems using
MapReduce applications
Debugging MapReduce Programs
Testing with MRUnit
Logging
Other Debugging Strategies.
Advanced MapReduce Programming
A Recap of the MapReduce Flow
The Secondary Sort
Customized Input Formats and Output
Formats
Sqoop
Getting Sqoop
A Sample Import
Database Imports
Controlling the import
Imports and consistency
Direct-mode imports
Performing an Export
CDH4 Enhancements
Name Node High – Availability
Name Node federation
Fencing
YARN

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