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Big Data - Hadoop

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Module 1: Hadoop Installation & setup
Hadoop 2.x Cluster Architecture 
Federation and High Availability
A Typical Production Cluster setup
Hadoop Cluster Modes      
Common Hadoop Shell Commands 
Hadoop 2.x Configuration Files       
Cloudera Single node cluster 
Hive
Pig 
Sqoop 
Flume 
Scala 
Spark

Module 2: Introduction to Big Data Hadoop. Understanding HDFS & Map reduce
Introducing Big Data & Hadoop 
What is Big Data and where does Hadoop fits in,
Two important Hadoop ecosystem components namely Map Reduce and HDFS
In-depth Hadoop Distributed File System – Replications
Block Size 
Secondary Name node
High Availability 
In-depth YARN – Resource Manager 
Node Manager
Hands-on Exercise 


Module 3: Deep Dive in Map reduce
Detailed understanding of the working of Map Reduce, 
Mapping and reducing process 
The working of Driver 
Combiners
Partitioners 
Input Formats 
Output Formats
Shuffle 
Sor
Hands-on Exercise 


Module 4: Introduction to Hive
Introducing Hadoop Hive 
Detailed architecture of Hive 
Comparing Hive with Pig and RDBMS 
Working with Hive Query Language
Creation of database
Table 
Group by and other clauses 
The various types of Hive tables
H catalog 
Storing the Hive Results 
Hive partitioning and Buckets
Hands-on Exercise 


Module 5: Advance Hive & Impala
 Indexing in Hive
Map side Join in Hive 
Working with complex data types 
•            Hive User-defined Functions
Introduction to Impala 
Comparing Hive with Impala 
Detailed architecture of Impala

Module 6: Introduction to Pig
Apache Pig introduction 
Apache Pig features 
The various data types and schema in Hive 
The available functions in Pig 
Hive Bags 
Tuples and Fields
Hands-on Exercise 


Module 7: Flume, Sqoop & HBase
Introduction to Apache Sqoop 
Sqoop overview 
Basic imports and exports, 
How to improve Sqoop performance
Limitation of Sqoop 
Introduction to Flume and its Architecture 
Introduction to HBase 
The CAP theorem.
Hands-on Exercise 


Module 8: Writing Spark Applications using Scala
Using Scala for writing Apache Spark applications 
Detailed study of Scala 
The need for Scala
The concept of object oriented programing
Executing the Scala code
Various classes in Scala like Getters, Setters, Constructors and Abstract 
Extending Objects
Overriding Methods 
The Java and Scala interoperability
The concept of functional programming and anonymous functions,
Bobs rockets package 
Comparing the mutable and immutable collections
Hands-on Exercise 


Module 9: Spark framework
Detailed Apache Spark 
Apache Spark features 
Comparing with Hadoop
The various Spark components
Combining HDFS with Spark 
Scalding 
Introduction to Scala 
Importance of Scala and RDD
Hands-on Exercise 

Module 10: RDD in Spark
The RDD operation in Spark
The Spark transformations
Actions 
Data loading 
Comparing with Map Reduce 
Key Value Pair
Hands-on Exercise 

Module 11: Data Frames and Spark SQL
The detailed Spark SQL
The significance of SQL in Spark for working with structured data processing
Spark SQL JSON support
Working with XML data 
Parquet files 
Creating HiveContext 
Writing Data Frame to Hive 
Reading of JDBC files 
The importance of Data Frames in Spark 
Creating Data Frames 
Schema manual inferring 
Working with CSV files 
Reading of JDBC tables
Converting from Data Frame to JDBC
The user-defined functions in Spark SQL
Shared variable and accumulators 
How to query and transform data in Data Frames 
How Data Frame provides the benefits of both Spark RDD and Spark SQL 
Deploying Hive on Spark as the execution engine
Hands-on Exercise 


Module 12: Machine Learning using Spark (Mlib)
Different Algorithms
The concept of iterative algorithm in Spark
Analyzing with Spark graph processing
Introduction to K-Means and machine learning 
Various variables in Spark like shared variables
Broadcast variables 
Learning about accumulators
Hands-on Exercise 

Module 13: Spark Streaming
Introduction to Spark streaming
The architecture of Spark Streaming
Working with the Spark streaming program 
Processing data using Spark streaming 
Requesting count and Dstream
Multi-batch and sliding window operations and working with advanced data sources
Hands-on Exercise 

Module 14: Hadoop Administration – Multi Node Cluster Setup using Amazon EC2
Create a four node Hadoop cluster setup
Running the Map Reduce Jobs on the Hadoop cluster
Successfully running the Map Reduce code 
Working with the Cloudera Manager setup
Hands-on Exercise 


Module 15: Hadoop Administration – Cluster Configuration
The overview of Hadoop configuration 
The importance of Hadoop configuration file 
Various parameters and values of configuration 
HDFS parameters and MapReduce parameters 
Setting up the Hadoop environment 
Include and Exclude configuration files 
Administration and maintenance of Name node 
Data node directory structures and files 
File system image and Edit log
Hands-on Exercise 

Module 16: Hadoop Administration – Maintenance, Monitoring and Troubleshooting
Introduction to the Checkpoint Procedure 
Name node failure and how to ensure the recovery procedure 
Safe Mode 
Metadata and Data backup 

The various potential problems and solutions 


Module 17: ETL Connectivity with Hadoop Ecosystem
How ETL tools work in Big data Industry
Introduction to ETL and Data warehousing
Working with prominent use cases of Big data in ETL industry
End to End ETL PoC showing big data integration with ETL tool
Hands-on Exercise 

Module 18: Project Solution Discussion and Cloudera Certification Tips & Tricks
Working towards the solution of the Hadoop project solution
Its problem statements and the possible solution outcomes
Preparing for the Cloudera Certifications
Points to focus for scoring the highest marks 
Tips for cracking Hadoop interview questions
Hands-on Exercise 








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100% Placement Assistance

We Make Fresher, industry-ready software professionals
Hands on Project Experience exposures in the Lab session
Real Time case studies to practice
Free Technical Support after Course Completion
Back up Classes Available
LAB Facility 
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Latest Study Material
Fast Track and Normal Batches available

REVIEWS



Sagar Wadhwani at

Excellent course and very knowledgeable teacher.Thank you!

Himanshu Maheshwari at

Simple and easy, gives ample understanding on what and how big data works for experienced beginners.

Raman Oswal at

Quite Informative for beginners in a simple language. Quickly understood.

FREQUENTLY ASKED QUESTIONS

Yes, We provide 100% Job Assistance to all IEVISION students. 
A dedicated HR – Recruitment members are designated to assist you in preparing your professional resume building, guiding you on HR Interview Process, Sending your resumes to corporates and assist you till you get placed.
Since last 6 years, IEVISION Students are placed in many countries and most of the MNC companies in India.

After course completion, Participation Certificate will be awarded.  We do also support in getting the Global Certification, Please connect with our support staff and you will be assisted.

We are operating from central location in Pune. Visit Contact us www.ievision.in  
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IEVISION Trainers are working professionals, highly experienced, certified on various levels on particular technologies with hands-on industry experience. Trainers are motivated to build the strong technical capability of students to achieve their objectives in life.
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