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Real Time Big Data & Hadoop Training- Tecwallet

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Real Time Big Data & Hadoop Training

BigData & Hadoop Course

The theoretical and practical mix of this course has the following focus:

ï‚· To explore the fundamental concepts of big data analytics

ï‚· To develop in-depth knowledge and understanding of the big data analytic domain.

ï‚· To learn to analyze the big data using intelligent techniques.

ï‚· To use advanced analytical tools/ decision-making tools/ operation research techniques to analyze the complex problems and get ready to develop such new techniques for the future..

ï‚· Master of understanding the concept of the Big Data & Hadoop framework.

ï‚· Acquire in-depth to understanding with several other types of data which store in Big Data & Hadoop.

ï‚· Understanding the methods on how to Big Data & Hadoop deployment in a cluster environment and infrastructure.

ï‚· Master the core and advanced concepts of the Hadoop Ecosystem, including HDFS & Map- Reduce frameworks. ï‚· Get hands-on experience in setting up a single node Hadoop cluster.

ï‚· Master with distinct other components of Hadoop Ecosystem.

ï‚· Performer Data Analytics using PIG & HIVE.

ï‚· Best implementation of Hadoop project.

ï‚· Real-life working experience on an industry based project on Big Data Analytics using the Hadoop Ecosystem and much more.

With the number of Big Data & Hadoop careers are on the rise, this course is fast becoming the must-know technology for the following professionals:

ï‚· Data Architects

ï‚· Data Engineer

ï‚· Technical Engineer

ï‚· Data Analyst

ï‚· Data Integration Architects

ï‚· Tech Managers

ï‚· Decision Makers

ï‚· Database Administrators

ï‚· Java Developers/ Any other developers

ï‚· Technical Infrastructure Team

ï‚· Any working professional interested in knowing Hadoop

ï‚· Any graduate/post-graduate with an urge to learn Hadoop

Familiarity with core java will be an advantage, but is not mandatory.

Familiarity with any database will be an advantage, but is not mandatory.


 Introduction to Big Data
 Big Data Definition
 Significance -Why Big Data?
 At what rate data is moving towards BigData?
 How single person contributing towards BigData?
 Role of BigData in day today life
 Why RDBMS is not suited for BigData
 Drawbacks of RDBMS
Case Study description for BigData Giant companies like Facebook, Google, Amazon, uber
ï‚· Introduction to Hadoop
ï‚· Hadoop -History
ï‚· Hadoop Architecture
ï‚· Why is Hadoop Important?
ï‚· How are files stored in Hadoop?
ï‚· Hadoop Components
ï‚· Hadoop Ecosystem
ï‚· Block Allocation in HDFS
ï‚· HDFS Architecture
ï‚· HDFS Read Operation
ï‚· HDFS Write Operation
ï‚· When to use and not use HDFS
ï‚· Advantages of Hadoop
ï‚· Drawbacks in Hadoop 1.0
ï‚· Introduction to Hadoop 2.0
ï‚· Yarn Architecture
ï‚· Yarn Components
ï‚· YARN Ecosystem
ï‚· Difference between Hadoop 1.0 and 2.0
Hands-on Exercises on Cloudera 5.10 and Software Installation
ï‚· MapReduce Concept
ï‚· MapReduce Components
ï‚· MapReduce Architecture
ï‚· MapReduce Internals
ï‚· Mapper, Reducer, Driver
ï‚· Understanding Mapper
ï‚· Understanding Reducer
ï‚· Shuffler, Sort
ï‚· Practitioner
ï‚· Combiner
ï‚· Running a MapReduce Job
Hands-on Exercises on word count Job using Jar files
ï‚· Introduction to Pig
ï‚· Pig History
ï‚· Pig Architecture
ï‚· Pig Components
ï‚· Pig Latin Basics
ï‚· Data Loading and Storing in Pig
ï‚· Filtering in Pig
ï‚· Data Transformation in Pig
ï‚· Grouping and Sorting in Pig
ï‚· Advanced Features
ï‚· Joins in Pig &User Defined Functions
Hands-on Exercises on different data-set and basic loading operation with complex approach algorithms
ï‚· Introduction to Hive
ï‚· Hive History
ï‚· Hive Architecture
ï‚· Hive Components
ï‚· Data Storage in Hive
ï‚· Data Types in Hive
ï‚· Hive Query Language Features
ï‚· Partitions in Hive
ï‚· Joins in Hive
Hands-on Exercises on different data-set and basic loading operation with complex algorithm approach.
ï‚· Introduction to Sqoop
ï‚· Sqoop Overview
ï‚· Data Import and Export
ï‚· Need for Sqoop
ï‚· Uses for Sqoop
ï‚· Advantages for Sqoop
Hands-on Exercises to load data from MySQL to HDFS and HDFS to MySQL using different attributes.
ï‚· Introduction to Apache Impala
ï‚· Apache Impala Architecture
ï‚· Apache Impala Features
ï‚· Uses Apache Impala
ï‚· Advantages Apache Impala
ï‚· Comparing Apache Impala and Apache Hive
Hands-on Exercises on different Dataset and building up solution for certain business problem
ï‚· Introduction to Apache Oozie
ï‚· Apache Oozie Architecture
ï‚· Apache Oozie workflow
ï‚· Understating use case for Apache Oozie
Hands-on Exercises and understand to schedule job in Hive using Apache Oozie
ï‚· Understanding Apache Flume
ï‚· Architecture Apache Flume
ï‚· Source Concept
ï‚· Sink Concept
ï‚· Channel Concept
Extract Data from Twitter
Understanding Cloudera Manager


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Start Date:10/06/2017
End Date:TBD
Organiser: Rahul Anand
Category: BIGDATA, HADOOP, Tableau

Rahul Anand  

Enrolled Students : 0

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