Apache Hadoop Administration Training Certification Course , Bhubaneswar

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  8989 Learners

Big Data industry is growing at a rate of 10% each year. The big data administration training offered by AcadGild has been such designed to provide adequate exposure to big data for beginners as well as experienced professionals and gain sufficient proficiency over core components of big data, Apache tools, and Hadoop ecosystem.

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Course Overview
Introduction to Big Data Administration
Get introduced to big data, big data problems in real world, unstructured data, big data processing issues and job trends in big data.
Comprehensive Understanding The Steps to Operate and Maintain a Hadoop Cluster
Learn how to install, configure, manage the Hadoop platform and deploy, manage, monitor Hadoop Clusters. Gain an in-depth knowledge on Hadoop architecture and administration.
Big Data Core Components & Apache Tools
Get well versed with the Hadoop components - HDFS, MapReduce and Apache tools - Sqoop and Flume, Hbase set up and architecture, Pig, Hive, Apache Oozie, MapR fundamentals. Set up a real time Hadoop cluster. Understand backup recovery, upgradation and maintenance.
Advanced Hadoop Admin concepts
Gain expertise in setting up real Hadoop cluster, planning considerations, hardware planning, back up of cluster data, Distcp, quota management and trash server. Learn about secondary namenode, safemode in Hadoop, user quota management, decommissioning and commissioning of node, upgrade process, Hadoop security and high availability with Quorum Journal Manager.
Work with Real world Hadoop Projects and Various Hadoop distribution
Gain hands-on expertise of working on a real-time project. Work using Cloudera Manager and Apache Ambari, Apache Hadoop installation and cluster setup on AWS EC2 (Linux).
Highly Experienced
Mentors
Lifetime access to dashboard
Develop 2 real time projects in big data
24X7 coding
support
Free job preparation week
Course Syllabus
  • Introduction to Big Data
  • Big Data problem
  • Unstructured data
  • Big Data processing issues
  • Job trends in Big Data
  • Hadoop the problem and solution
  • Advantages of Hadoop
  • Hadoop components
  • HDFS
  • MapReduce
  • File write and read in HDFS
  • Hadoop 1.0 summary
  • How job tracker works?
  • Hadoop installation local mode
  • Pseudo distributed mode
  • Hadoop configuration files
  • Accessing the web UI
  • Apache Hadoop installation and cluster setup on AWS EC2 (Ubuntu)
  • How to launch an Amazon AWS EC2 instance
  • Planning considerations
  • Hardware planning
  • Why not RAID?
  • Real world use cases
  • Planning for Hadoop cluster software
  • Different Hadoop distributions
  • Cloud based distributions
  • Jobs and schedulers
  • The fair scheduler
  • Backing up the cluster data
  • Distcp
  • Quota management and trash server
  • More about secondary namenode
  • Safemode in Hadoop
  • User quota management
  • Decommissioning a node
  • Commissioning a node
  • The upgrade process
  • Hadoop security
  • Revisiting Hadoop 1
  • Drawbacks of Hadoop 1
  • Features of Hadoop 2
  • YARN
  • Process flow
  • Hadoop 1 configuration files
  • Hadoop 2 configuration files
  • Deprecated properties
  • Core configuration files
  • Individual files
  • Secondary NameNode
  • HA options
  • Quorum journal manager
  • HA with shared NFS
  • Current HDFS set up and drawbacks
  • Federation and concepts
  • Key benefits
  • Demonstration
  • Mentee can select project from predefined set of AcadGild projects or they can come up with their own ideas for their projects
  • Mentee can select project from predefined set of AcadGild projects or they can come up with their own ideas for their projects
  • Data loading techniques
  • What is SQOOP
  • SQOOP configuration
  • What is FLUME
  • FLUME components
  • Introduction to NoSQL
  • HBase features
  • HBase VS RDBMS
  • HBase architecture
  • Use case
  • Pig:Need for PIG
  • PIG concepts
  • Installation and configuration;Hive:Hive Architecture
  • Hive as a data warehousing tool
  • Need for Oozie
  • Oozie workflow
  • Oozie cordinator
  • Limitations of HDFS
  • Compare MapR FS with HDFS
  • Comparing storage
  • Containers and volumes
  • Replication and snapshots
  • Cloudera Hadoop
  • The importance of cloudera manager
  • Installation
  • Features
  • Advantages
  • Mentee can select project from predefined set of AcadGild projects or they can come up with their own ideas for their projects
  • Mentee can select project from predefined set of AcadGild projects or they can come up with their own ideas for their projects
  • Mentee can select project from predefined set of AcadGild projects or they can come up with their own ideas for their projects
  • Mentee can select project from predefined set of AcadGild projects or they can come up with their own ideas for their projects
Projects Which Students Will Develop
Creating a 6 Node Hadoop Cluster
Create a 6 Node Hadoop cluster with one separate machine for NameNode, Job Tracker and Secondary NameNode with 3 machines running as slave nodes and configure the default Secondary NameNode snapshot interval as 3 hours.
Creating a 6 Node Hadoop Cluster
Create Hadoop Federation
Create a 4 node Hadoop cluster with Sqoop, MySQL installed in it and implement the federation feature in the cluster and check the federation feature by letting NameNode down.
Create Hadoop Federation
Create Hadoop Cluster With All Its Ecosystems (Pig, Hive, Oozie)
Create a 5 node Hadoop cluster with Hadoop 2.6.0 installed. The block size needs to be 128 MB. The Resource Manager should run in port 60300. Configure 3 data nodes. Create a client machine. Install Pig and Hive in client machine and Install and setup Oozie. Test the cluster by launching sample Hive and Pig jobs.
Create Hadoop Cluster With All Its Ecosystems (Pig, Hive, Oozie)
Create High Availability Hadoop Cluster
Create a 4 node Hadoop cluster with Hadoop 2.6.0 installed with high availability enabled on the cluster using Quorum Journal Manager and check the health of Zookeeper and high availability functionality.
Create High Availability Hadoop Cluster
Create Multi-node HBase Cluster.
Create a 4 node Hadoop cluster with built-in Zookeeper with HBase. The cluster should have one Namenode, one Resource Manager and two Datanodes. Datanode 1 and 2 will be region servers.
Create Multi-node HBase Cluster.
Job Preparation Week
After you complete your course, our unique job preperation solution makes sure you can check out all the essentials of your job preperation checklist, right from your resume to your interview skills.
In-depth Mock Interviews
With 2 In depth mock interviews, you are at complete edge over the others.
Resume Building And Interview Questions
Resume builds the first impression and we help you build a resume that stands out.
Online Reputation Building
Helps build a strong online presence in LinkedIn, Git, Stack Overflow and many more.
Resume Sharing With Top Employers
Your resume is shared with top employers, so that you find your dream job.
30+
Offers Made
To Students
2500+
Hours Spent
Coding
100+
Recommendations
Given By Clients
500+
Projects Completed
By Students
Places you could land up to
Customers Feedback
FAQ's
The big data and Hadoop administration course is designed to provide knowledge and skills to become a successful Hadoop Administrator. It starts with the fundamental concepts of Apache Hadoop and Hadoop cluster and covers topics to create, configure, manage, monitor and secure a Hadoop cluster. The course will also cover HBase administration so after completion of Hadoop administration course one will be prepared to understand and solve problems that you may come across while working on Hadoop cluster.
This course is suitable for Systems Administrators, Windows Administrators, Linux Administrators, Infrastructure Engineers, DB Administrators, Big Data Architects, Mainframe Professionals and IT managers who are interested in learning Hadoop Administration.
This course will help you in starting your career in Hadoop Administration as well as sharpen your Linux and Administration skills to build and maintain the Hadoop cluster for existing big data projects.
No prior Java knowledge is required to learn Big Data Hadoop Administration. This course is suitable for Linux Administrators, Database Administrators, and Networking professionals.
A Data Scientist takes the business need and as per the need prepares the plan to implement the analytics project. Data scientist possesses both the skills of a software engineer and an applied scientist. On the other hand, Big Data System Administrator or Big Data Administrator’s role is to construct, install, and maintain the cluster as well as add new components in existing Big Data cluster.
Basic understanding of Linux operating systems is necessary. In case any candidate does not have the necessary knowledge then we do provide Linux Primer course which covers essentials required to learn Hadoop Administration for free.
Linux (centos), Apache Hadoop, Cloudera Manager, Apache HBase, Apache Pig, Apache Hive.
Mentors are qualified developers in the field with a minimum of 4+ years of experience. A love for coding and a passion for teaching are essential prerequisites in all our mentors
All you need is a Windows or Mac machine and an Internet connection of minimum speed of 500Kbps.
Besides the classes, spending around 3 hours each day will be enough.
If you decide to leave within the first week of class starts, we refund fully. If you decide to leave before the class starts, 50% of the total paid fee would be deducted and the remaining amount will be refunded to the user. The refund policy would be applied if the total amount paid is more than 50% of the course fees. If a user is opting for a complementary course, then the refund policy would be applied only on the 1st course.
The classes are held on weekends as well as on weekdays. You can enroll for a batch that is convenient to suit your personal schedule.