Certified Big Data and Apache Hadoop Developer

Course details

Vskills certification for Big Data and Apache Hadoop Developer Certification assesses the knowledge and skills required to become a successful Hadoop Developer, Administrator, Data Scientist Professional etc in the field of Big Data. The certification tests the candidates on various areas in Big Data and Apache ;Knowledge of object -oriented programming and Java programming language is pre-requisite for the certification.

Why should one take this certification?
This Course is intended for professionals and graduates wanting to excel in their chosen areas. It is also well suited for those who are already working and would like to take certification for further career progression.

Earning Vskills Big Data and Apache Hadoop Developer Certification can help candidate differentiate in today's competitive job market, broaden their employment opportunities by displaying their advanced skills, and result in higher earning potential.

Who will benefit from taking this certification?
The course is designed for professionals aspiring to make a career in Big Data and Hadoop Framework. Students, Software Professionals, Analytics Professionals, ETL developers, Project Managers, Architects, and Testing Professionals are the key beneficiaries of this course. Other professionals who are looking forward to acquire a solid foundation on Big Data Industry can also opt for this course. This not only improves their skill set but also makes their CV stronger and existing employees looking for a better role can prove their employers the value of their skills through this certification.

Table of Contents

1. Big Data

• Big Data Definition
• Big Data Types
• Big Data Source
• Big Data Challenges
• Big Data Benefits
• Big Data Applications
• Netflix Application

2. Apache Hadoop

• Introduction
• Advantages & Disadvantages
• History of Hadoop Project
• Need for Hadoop
• Hadoop Architecture
• RDBMS vs Hadoop
• Vendor Comparison
• Hardware Recommendations
• Hadoop Installation

3. HDFS

• Basics (Blocks, Namenodes and Datanodes)
• HDFS Architecture
• Data Read and Write Process
• HDFS Permissions
• Data Replication
• HDFS Accessibility
• HDFS Filesystem Operations
• HDFS Interfaces
• Heartbeats
• Rack Awareness
• distcp

4. MapReduce

• MapReduce Basics
• MapReduce Work Flow
• MapReduce Framework
• Hadoop Data Types
• MapReduce Internals
• Job Formats
• Debugging and Profiling
• Distributed Cache
• Combiner Functions
• Streaming
• Counters, Sorting and Joins

5. YARN

• YARN Infrastructure
• ResourceManager
• ApplicationMaster
• NodeManager
• Container

6. Pig

• Pig Architecture
• Installation and Modes
• Grunt and Pig Script
• Pig Latin Commands
• UDF and Data Processing Operator

7. HBase

• HBase Architecture
• HBase Installation
• HBase Configuration
• HBase Schema Design
• HBase Commands
• MapReduce Integration
• HBase Security

8. Sqoop and Flume

• Sqoop
• Flume

9. Hive

• Hive Architecture
• Hive shell
• Hive Data types
• HiveQL

10. Workflow

• Apache Oozie

11. Hadoop Cluster Management

• Cluster Planning
• Installation and Configuration
• Testing
• Benchmarking
• Monitoring

12. Administration

• dfsadmin, fsck and balancer
• Logging
• Data Backup
• Add and removal of nodes

13. Security

• Authentication
• Data Confidentiality
• Configuration

14. NextGen Hadoop

• HDFS HA
• HDFS Federation

Updated on 31 January, 2019