Big Data & Hadoop

Administrator schooling course for apache hadoop affords members with a comprehensive know-how of all of the steps vital to perform and hold a hadoop cluster. The course subjects consist of creation to hadoop and its structure, mapreduce, and hdfs and mapreduce abstraction. From installation and configuration via load balancing and tuning, this education direction is the first-class preparation for the real-international challenges faced by means of hadoop directors. It in addition covers nice practices to configure, set up, administer, hold, screen and troubleshoot a hadoop cluster.

What can I learn in this Course?

  • Apprehend hadoop fundamental components and architecture.

  • deep dive into hadoop distributed record gadget (hdfs).

  • recognize standards of yarn.

  • understand mapreduce abstraction and its working.

  • plan and deploy a hadoop cluster.

  • optimize hadoop cluster for high performance, based totally on specific activity necessities.

  • Screen hadoop cluster and execute habitual management processes.

  • Take care of hadoop issue failures and recoveries

  • determining the precise hardware and infrastructure to your cluster

  • a way to load statistics into the cluster from dynamically-generated documents the use of flume and from rdbms using sqoop
    configuring the fair scheduler to offer carrier-degree agreements for a couple of users of a cluster.

  • great practices for getting ready and keeping apache hadoop in manufacturing.

  • troubleshooting, diagnosing, tuning, and solving hadoop issues.

 Topics Covered

1. Introduction

  • Why Hadoop?

  • Fundamental Concepts

  • Core Hadoop Components  


2.Hadoop Cluster Installation

  • Rationale for a Cluster Management Solution

  • Hadoop Manager Features

  • Hadoop manager set up

  • hadoop (cdh) installation

3. the hadoop dispensed file machine (hdfs)

  • hdfs featuresowriting and reading files

  • namenode memory considerations

  • evaluate of hdfs safety

  • web united states of americafor hdfsousing the hadoop document shell.


​4. MapReduce and Spark on YARN

  • The Role of Computational Frameworks

  • YARN: The Cluster Resource Manager

  • MapReduce Concepts

  • Apache Spark Concepts

  • Running Computational Frameworks on YARN

  • Exploring YARN Applications through the Web UIs, and the Shell YARN Application Logs

5.Hadoop Configuration and Daemon Logs

  • Manager Constructs for Managing Configurations

  • Locating Configurations and Applying Configuration Changes

  • Managing Role Instances and Adding Services

  • Configuring the HDFS Service

  • Configuring Hadoop Daemon Logs

  • Configuring the YARN Service

6.Getting Data into HDFS

  • Ingesting Data From External Sources With Flume

  • Ingesting Data From Relational Databases With Sqoop

  • REST Interfaces

  • Best Practices for Importing Data



7.Planning Your Hadoop Cluster

  • General Planning Considerations

  • Choosing the Right Hardware

  • Virtualization Options

  • Network Considerations

  • Configuring Nodes

8.Installing and Configuring Hive, Impala, and Pig

9.Hadoop Clients Consisting of hue

  • what are hadoop customers? Installing and configuring hadoop clients

  • putting in and configuring hue

  • hue authentication and authorization

  • advanced cluster configuration

  • superior configuration parameters

  • configuring hadoop ports

  • configuring hdfs for rack attention

  • configuring hdfs excessive availability

10. Advanced Cluster Configuration

  • advanced configuration parameters

  • configuring hadoop ports

  • configuring hdfs for rack attention

  • configuring hdfs excessive availability

11.Hadoop Security

  • why hadoop safety is essential

  • hadoop’s protection gadget concepts

  • what kerberos is and the way it works

  • securing a hadoop cluster with kerberos

  • different protection standards