About Course
Traditional Data storage techniques can’t store or process large volume of structured and unstructured Data sets. Those data are referred as Big Data. Hadoop on the other hand is a tool that is used to handle big data. It is an open-source framework manufactured by the Apache Software Foundation.
Affluenz IT Academy will emphasize on how to design distributed applications to manage “Big Data” using Hadoop. This will also detail how to use Pig and Spark to write scripts in order to process data sets on Hadoop cluster
Key Features
- 40 hours of instructor led online training
- Certificate of completion
- 2 Live Industry Project
- Dedicated awareness session from Industry experts
Online Classroom with virtual Internship
- 3 Weeks’ course
- 1 Week Live Project Internship
- Certificate of completion
- Certficate of Internship
9999/-
Online Classroom with Corporate Internship
- 6 Weeks’ course
- 2 Week Live Project Internship
- Certificate of completion
- Certficate of Internship
14,999/-
Why Big Data & Hadoop
According to IDC, by 2020, organizations that can analyze all relevant data and deliver actionable information will earn $430 billion more than their less analytically oriented peers. As of today, there is a surplus demand for people who are Hadoop 2.0 certified
Who should take this Big Data & Hadoop Course?
Who should take this Big Data & Hadoop Course?
Affluenz IT Academy crafts this course to all who aspire to build a career in Big Data like Data Scientist, AI Engineer, Business Analyst, Information architect and so on. We recommend this course to
- Developers aspiring to be a data scientist or machine learning engineer
- Analytics managers who are leading a team of analysts
- Business analysts who want to understand data science techniques
- Information architects who want to gain expertise in machine learning algorithms
- Analytics professionals who want to work in machine learning or artificial intelligence
- Graduates looking to build a career in data science and machine learning
- Experienced professionals who would like to harness machine learning in their fields to get more insights
Pre-requisites of Big Data & Hadoop
Pre-requisites of Big Data & Hadoop
Participants in this online course should have:
- Basic understanding on Data structure and Data Science
- Basics of statistics and mathematics
Course Preview
4 Weeks Course
- Module 1: Introduction of Big Data
1a> What is Big Data
1b> Big Data Learning Techniques
1c> 5C Architecture – connection, conversion, cyber cognition and configuration
- Module 2: Introduction to Hadoop
2a> Basics of Hadoop
2b> Hadoop Ecosystem
2c> 5C Architecture – connection, conversion, cyber cognition and configuration
- Module 3: Hadoop components
3a> 2.x Core Components
3b> Hadoop Storage: HDFS (Hadoop Distributed File System)
3c> Hadoop Processing: MapReduce Framework
- Module 4: Hadoop architecture and HDFS
4a> Hadoop 2.x Cluster Architecture
4b> Federation and High Availability Architecture
4c> Typical Production Hadoop Cluster
4d> Hadoop Cluster Modes
4e> Common Hadoop Shell Commands
- Module 5: Live Project on Big Data & Hadoop
5a> Assessment
5b> Live Project
- Module 6: Certification & Closure
6a> Certification of Completion
6b> Review and Feedback
8 Weeks Course
- Module 1: Introduction of Big Data
1a> What is Big Data
1b> Big Data Learning Techniques
1c> 5C Architecture – connection, conversion, cyber cognition and configuration
- Module 2: Introduction to Hadoop
2a> Basics of Hadoop
2b> Hadoop Ecosystem
- Module 3: Hadoop components
3a> 2.x Core Components
3b> Hadoop Storage: HDFS (Hadoop Distributed File System)
3c> Hadoop Processing: MapReduce Framework
- Module 4: Advanced Hadoop architecture and HDFS
4a> Hadoop 2.x Cluster Architecture
4b> Federation and High Availability Architecture
4c> Typical Production Hadoop Cluster
4d> Hadoop Cluster Modes
4e> Common Hadoop Shell Commands
4f> Hadoop 2.x Configuration Files
4g> Single Node Cluster & Multi-Node Cluster set up
4h> Basic Hadoop Administration
- Module 5: Hadoop MapReduce Framework
5a> Traditional way vs MapReduce way
5b> Why MapReduce
5c> YARN Components
5d> YARN Architecture
5e> YARN MapReduce Application Execution Flow
5f> YARN Workflow
5g> Anatomy of MapReduce Program
5h> Input Splits, Relation between Input Splits and HDFS Blocks
5i> MapReduce: Combiner & Partitioner
5j> Demo of Health Care Dataset
5k> Demo of Weather Dataset
- Module 6: Live Project on Big Data & Hadoop
6a> Assessment
6b> Live Project
- Module 7: Certification & Closure
7a> Certification of Completion
7b> Internship Certificate
7c> Review and Feedback
Sample Certification
- Certificate of Completion