Big Data Hadoop Certification Training Course

Big Data Hadoop Certification Training Course

Big Data Hadoop Certification Training Course

Almost five years ago, Google’s Eric Schmidt announced that we had reached the point where more data was being created every two days, than in all of human history, up until 2003.

Since then, companies of all shapes and sizes have been getting to grips with new ways of handling the incredible volume of information that is becoming available to us every day.

For example, users of Facebook upload around one billion pieces of content to the social network site every day. In industry, machinery and vehicles are fitted with sensors and trackers that record their every move, and whenever we call a call centre, an audio recording of our conversation is made, and stored in a huge digital database. Big Data Hadoop Certification Training Course help you elaborate these.

In addition, whenever we go online (as most of us increasingly do for a number of reasons – shopping, socialising, making travel arrangements) we leave behind a digital footprint – a record of websites we visit, products viewed, even how long we leave the mouse cursor over certain areas of the screen, in some circumstances.

We collectively refer to both these huge datasets we are building, and the practice of interpreting, analysing and acting upon insights gleaned from this information, as “big data” – and it is changing the world we live in.

But Big Data is not just for the big boys, it matters to every company – no matter how small or traditional. To cater for this huge demand many companies have sprung up to offer services to other businesses, enabling them to launch big data initiatives of their own. In other words, to leverage the information they have available to improve effectiveness and efficiencies in their business, and ultimately increase profits.Big Data Hadoop Certification Training Course help you learn all these in detail.

A lot of the software and analytics tools needed to carry out big data analysis are built on open source principles – meaning they are essentially in the public domain and free for anyone to use for any purpose.

For example, Hadoop is a framework – a collection of software tools and applications – designed to allow organisations of any size to store and analyse huge amounts of information. It is designed to run on cheap, commonly-available hardware rather than expensive, specialist equipment that would previously have been necessary.

Companies including Amazon, Google, IBM, HP, as well as newer names such as Hortonworks, MapR and Cloudera offer big data solutions and support, as well as tailored versions of the free products, designed to work out-of-the-box and with less complex setup requirements. This also enables companies to minimise infrastructure investments or avoided completely by using cloud-based storage and analysis tools that can be rented when needed.

Big Data Hadoop Certification Training Course is best designed to suit your needs and customized for everyone.

Course Name :Big Data Programming

Duration : 40 Hours

Hadoop has the ability to store as well as process bulks of data in any format. With data volumes going larger day by day with the evolution of social media, considering this technology is really, really important.

Unmatched computing power: The distributed computing model of Hadoop processes big data in a fast pace. The more computing nodes, the more processing power.

Effective fault tolerance: There is no need to panic in hardware failure as Hadoop has the facility to protect data and applications. In case a node fails, jobs are automatically redirected to other nodes hence no obstruction in distributed computing. It also stores multiple copies of data.

Superb flexibility: There is no need to preprocess data before its storage just you used to do in conventional relational databases. You can store as much data as you want and use it later. Unstructured, text, images and videos can also be stored easily.

Scalability: By adding nodes you can enhance your system to handle more data. There is no need to be a pro in system administration.

Affordable: As the open source network is free, it uses commodity hardware for the storage of large data.

Big Data Hadoop Certification Training Course Highlights

  1. Hands-on Training
  2. Course Contents as suggested by HP
  3. Instructor led Training
  4. Relevant Project Work
  5. HP Certificate
  6. Training by Highly Experienced Professional

Take Away – Big Data Hadoop Certification Training Course

This course covers the fundamentals of the powerful and versatile Hadoop platform and lays arm foundation for the development of your Hadoop knowledge to understand the meaning behind Big Data.

Target Audience – Big Data Hadoop Certification Training Course

Fresher’s with Computer Science knowledge, Administrators, System Engineers, Developers, and Project managers.

Pre-Requisites – Big Data Hadoop Certification Training Course

  • Computer Fundamentals,
  • Windows Operating System
  • Basics of Programming Language
  • Basics of Unix/Linux OS
  • Core Java
  • Basic SQL

Objectives : Big Data Hadoop Certification Training Course

  • Big Data Usage
  • Hadoop
  • Live Experience

Recommended Next Course

  • Certification Exams

Big Data Hadoop Certification Training Course Contents

1.The Motivation & Limitation for Hadoop

  • Problems with TraditionalLarge-Scale Systems
  • Why Hadoop&Hadoop Fundamental Concepts
  • History of Hadoop with Hadoopable problems
  • Motivation & Limitation of Hadoop
  • Available version Hadoop 1.x & 2.x
  • Available Distributions of Hadoop (Cloudera, Hortonworks)
  • Hadoop Projects & Components
  • The Hadoop Distributed File System (HDFS)

2.Hadoop Ecosystem& Cluster
Hadoop Ecosystem projects & Components overview 

  • HDFS – File System
  •  HBase – The Hadoop Database
  •  Cassandra – No-SQL Database
  •  Hive – SQL Engine
  • Mahout

Hadoop Architecture overview Cluster Daemons&Its Functions

  •  Name Node
  •  Secondary Node
  •  Data Nodes

3. Planning Hadoop Cluster& Initial Configuration

  • General Planning Considerations
  • Choosing the Right Hardware
  • Network Considerations
  • Configuring Nodes
  • Planning for Cluster & Its Management
  • Types of Deployment
  • Cloudera Manager

4. Installation &Deployment of Hadoop

  • Installing Hadoop (Cloudera)
  • Installation – Pig, Hive, HBase, Cassandra etc
  • Specifying the Hadoop Configuration
  • Performing Initial HDFS Configuration
  • Performing Initial YARN and MapReduce Configuration
  • Hadoop Logging&Cluster Monitoring

5. Load Data and Run Application

  • Ingesting Data from External Sources withFlume
  • Ingesting Data from Relational Databaseswith Sqoop
  • REST Interfaces
  • Best Practices for Importing Data

6. Manage, Maintain, Monitor, and troubleshoot of cluster

  • General System Monitoring
  • Monitoring Hadoop Clusters
  • Common Troubleshooting Hadoop Clusters
  • Common Misconfigurations
  • Managing Running Jobs
  • Scheduling Hadoop Jobs

7. Upgrade, Rolling andBackup

  • Cluster Upgrading
  • Checking HDFS Status
  • Adding and Removing Cluster Nodes
  • Name Node Meta Data Backup
  • Data Backup
  • Distributed Copy
  • Parallel Data Ingestion

8. Conclusion & FAQs

  1. Introduction to Hadoop and Big Data
  • Introduction to Big Data
  • Introduction to Hadoop
  • Why Hadoop & Hadoop Fundamental Concepts
  • History of Hadoop with Hadoopable problems
  • Scenarios where Hadoop is used
  • Available version Hadoop 1.x & 2.x
  • Overview of batch processing and real time data analytics using Hadoop
  • Hadoop vendors – Apache , Cloudera , Hortonworks
  • Hadoop services – HDFS , MapReduce , YARN
  • Introduction to Hadoop Ecosystem components ( Hive, Hbase, Pig, Sqoop, Flume, Zookeeper, Oozie, Kafka, Spark )
  1. Cluster setup ( Hadoop 1.x )
  • Linux VM installation on system for Hadoop cluster using Oracle Virtual Box
  • Preparing nodes for Hadoop and VM settings
  • Install Java and configure password less SSH across nodes
  • Basic Linux commands
  • Hadoop 1.x single node deployment
  • Hadoop Daemons – NameNode, JobTacker, DataNode, TaskTracker, Secondary NameNode
  • Hadoop configuration files and running
  • Important web URLs and Logs for Hadoop
  • Run HDFS and Linux commands
  • Hadoop 1.x multi-mode deployment
  • Run sample jobs in Hadoop single and multi-node clusters
  1. HDFS Concepts
  • HDFS Design Goals
  • Understand  Blocks and how to configure block size
  • Block replication and replication factor
  • Understand Hadoop Rack Awareness and configure racks in Hadoop
  • File read and write anatomy in HDFS
  • Enable HDFS Tash
  • Configure HDFS Name and space Quota
  • Configure and use WebHDFS ( Rest API For HDFS )
  • Health monitoring using FSCK command
  • Understand NameNode Safemode, File system image and edits
  • Configure Secondary NameNode and use checkpointing process to provide NameNode failover
  • HDFS DFSAdmin and File system shell commands
  • Hadoop NameNode / DataNode directory structure
  • HDFS permissions model
  • HDFS Offline Image Viewer
  1. MapReduce Concepts
  • Introduction to MapReduce
  • MapReduce Architecture
  • Understanding the concept of Mappers & Reducers
  • Anatomy of MapReduce program
  • Phases of a MapReduce progam
  • Data-types in Hadoop MapReduce
  • Driver, Mapper and Reducer classes
  • InputSplit and RecordReader
  • Input format and Output format in Hadoop
  • Concepts of Combiner and Partitioner
  • Running and Monitoring MapReduce jobs
  • Writing your own MapReduce job using MapReduce API
  1. Cluster setup ( Hadoop 2.x )
  • Hadoop 1.x Limitations
  • Design Goals for Hadoop 2.x
  • Introduction to Hadoop 2.x
  • Introduction to YARN
  • Components of YARN – Resource Manager, Node Manager, Application Master
  • Deprecated properties
  • Hadoop 2.x Single node deployment
  • Hadoop 2.x Multi node deployment
  1. HDFS High Availability and Federation
  • Introduction to HDFS Federation
  • Understand Name service ID and Block pools
  • Introduction to HDFS High Availability
  • Failover mechanisms in Hadoop 1.x
  • Concept of Active and StandBy NameNode
  • Configuring Journal Nodes and avoiding split brain scenario
  • Automatic and manual failover techniques in HA using Zookeeper and ZKFC
  • HDFS HAadmin commands
  1. YARN – Yet Another Resource Negotiator
  • YARN Architecture
  • Yarn Components – Resource Manager, Node Manager, Job History Server, Application Time LIne Server, MR Application Master
  • YARN Application execution flow
  • Running and Monitoring YARN Applications
  • Understand and Configure Capacity / Fair Schedulers in YARN
  • Define and configure Queues
  • Job History Server / Application Time Line Server
  • YARN Rest API
  • Writng and executing YARN applications
  1. Hive
  • Problems with No-SQL Database
  • Introduction & Installation Hive
  • Data Types & Introduction to SQL
  • Hive-SQL: DML & DDL
  • Hive-SQL: Views & Indexes
  • Hive User Defined Functions
  • Configuration to HBase
  • Hive Thrift Service
  • Introduction to HCatalog
  • Install and configure HCatalog services
  1. Apache Flume 
  • Introduction to Flume
  • Flume Architecture and Installation
  • Define Flume agents – Sink, Source and Channel
  • Flume Use cases
  1. Apache Pig
  • Introduction to Pig
  • Pig Installation
  • Accessing Pig Grunt Shell
  • Pig data Types
  • Pig Commands
  • Pig Relational Operators
  • Pig User Defined Functions
  • Configure PIG to use HCatalog
  1. Apache Sqoop
  • Introduction to Sqoop
  • Sqoop Architecture and installation
  • Import Data using Sqoop in HDFS
  • Import all tables in Sqoop
  • Export data from HDFS
  1. Apache Zookeeper
  • Introduction to Apache Zookeeper
  • Zookeeper stand alone installation
  • Zookeeper Clustered installation
  • Understand Znodes and Ephemeral nodes
  • Manage Znodes using Java API
  • Zookeeper four letter word commands
  1. Apache Oozie
  • Introduction to Oozie
  • Oozie Architecture
  • Oozie server installation and configuration
  • Design Workflows, Coordinator Jobs, Bundle Jobs in Oozie
  1. Apache Hbase
  • Introduction to Hbase
  • Hbase Architecture
  • HBase components – Hbase master and Region servers
  • Hbase installation and configurations
  • Create sample tables and queries on HBase
  1. Apache Spark / Storm / Kafka
  • Real Time data Analytics
  • Introduction to Spark / Storm / Kafka
  1. Cluster Monitoring and Management tools
  • Cloudera Manager
  • Apache Ambari
  • Ganglia
  • JMX monitoring and Jconsole
  • Hadoop User Experience ( HUE )

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