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Hadoop

Hadoop

Learn BigData Hadoop Online

Hadoop is a open source system that changes the way a company stores data, processess data, and how it analyzes that data. Hadoop facilitates multiple types of analytic workloads to run on the same set of data workload, parallely, on a huge cluster of machines. Hadoop also comprises an ecosystem of open source components that help perform activities on data stored into Hadoop System in different ways.

Application Developers

Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

Database Administrator

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes, and projects.

System Administrator

Earn official recognition for your work, and share your success with friends, colleagues, and employers.

What’s Included in the course

Course Duration: 20 Days

What do I get from this course?

More than 400 hours of learning with project work, role-plays, code challenges, peer collaboration. Comprehensive project and coding time all through and at the end of the program. Projects designed and implemented as in the real world.

  • Machine Learning Principles
  • Data Science
  • R
  • Python
  • Statistical Analysis & Data Visualization with Excel
  • Analysis & Visualization with T-SQL (SQL Server)
  • Advance Programming with R
  • Advance Programming with Python
  • MS Azure
  • MS Machine Learning Server and MUCH MORE

What are the requirements for joining in?

You can just start if you know High school level mathematics, some statistics. Better yet if you programmed (or) coded (or) worked with MS Excel. But don’t worry we will make this fun and exciting even for starters.

You will need a laptop/ desktop and connected to internet.

How will course happen?

This is a instructor-led classroom course. Our professional mentors will deliver the course, coursework, hands-on everything online. Participate with the co-learners, have your Q&A posted on forums. Get your queries answered on our learning platform.

Who will train me?

Our team of experienced data science experts with great academic experience will take you through this experience. They are geeky but also know how to make this a fun filled mix of learning, real time runs, coding challenges, presentations and many more.

Who should attend this course?

  • Anyone interested in Machine Learning.
  • Students who have at least high school knowledge in math and who want to start learning Machine Learning.
  • College students in college who want to start a career in Data Science.
  • Analysts who want to level up in Machine Learning.
  • People who are not satisfied with their job and who want to become a Data Scientist.
  • People who want to create added value to their business by using powerful Machine Learning tools.

Course Structure

Understanding Big Data and Hadoop
  • Computer Clusters
  • Distributed Computing
  • Apache Hadoop
  • Types of Analysis That Use Hadoop
  • Apache Hadoop Ecosystem
  • Apache Hadoop Core Components
  • Hadoop Storage: HDFS
  • Hadoop Processing
  • MapReduce Framework
  • Cloudera’s Distribution Including Apache Hadoop (CDH)
  • CDH Architecture

 

Hadoop Architecture and HDFS
  • HDFS: Characteristics
  • HDFS Deployments: High Availability (HA) and Non-HA
  • HDFS Key Definitions
  • NameNode (NN)
  • Secondary NameNode (Non-HA)
  • DataNodes (DN)
  • Checkpoint Node, and Backup Node
  • Storing and Accessing Data Files in HDFS
  • Data Replication Rack-Awareness in HDFS
  • Data Replication Process
Hadoop MapReduce Framework
  • MapReduce (MRv1) Architecture
  • MapReduce Phases
  • MapReduce Framework
  • Parallel Processing with MapReduce
  • MapReduce Jobs
  • Interacting with MapReduce

 

YARN
  • Apache Hadoop YARN: Overview
  • Resource Management Using YARN
  • MapReduce 2.0 (MRv2) or YARN (Yet Another Resource Negotiator) Architecture
  • YARN Daemons
Pig Latin
  • About Pig
  • MapReduce Vs Pig
  • Programming Structure in Pig
Hive
  • About Hive
  • Hive Vs Pig
  • Hive Architecture and Components
  • Metastore in Hive
  • Limitations of Hive
  • Comparison with Traditional Database
Impala
  • Cloudera Impala
  • Key Features of Impala
  • Supported Data Formats in Impala
  • Programming Interfaces in Impala
NOSQL Database HBase
  • Introduction to NoSQL
  • Databases and HBase
  • NoSql v/s RDBMS
  • NoSql Components
  • NoSql Architecture
  • Run Modes & Configuration
  • Hive to NoSQL export import
Sqoop
  • Apache Sqoop
  • Sqoop Components
  • Sqoop Features
  • Sqoop: Connectors
  • Importing Data into Hive
  • Sqoop: Advantages
  • Sqoop Syntax
  • Connections to different Databases using Sqoop
Flume
  • What is Flume?
  • Flume Architecture
  • Flume Sources (Consume Events)
  • Flume Channels (Hold Events)
  • Flume Sinks (Deliver Events)
  • Flume Data Flows
  • Configuring Flume
  • Exploring a flume*.conf File
Flume
  • What is Flume?
  • Flume Architecture
  • Flume Sources (Consume Events)
  • Flume Channels (Hold Events)
  • Flume Sinks (Deliver Events)
  • Flume Data Flows
  • Configuring Flume
  • Exploring a flume*.conf File
Solr
  • Apache Solr (Cloudera Search)
  • Key Capabilities
  • Features

Request More Information

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