Big Data and Hadoop Training

Why choose JNtech Networks for Big Data and Hadoop?

Big Data and Hadoop

It is an estimate of the McKinsey Global Institute, which over more than 1.7 million of vacancies will be there in next 2 to 3 years in big data and Hadoop. The data problem is one of the biggest problems in the current industry. It is not easy to handle such huge data easily, so that the industries are switching to the big data and Hadoop for the same. This is why, the demand of the IT professionals in Big Data and Hadoop is increasing.

JNtech Networks is a training institute in India, Providing the best training in the Big Data and Hadoop at the best level. The Trainers in the JNtech Networks are the IT professionals, who have a long term experience in the same field.

Big Data and Hadoop Training Content

  • What is Data
  • Types of Data
  • What is BigData
  • Sources of BigData
  • Traditional way to Store Bigdata
  • Characteristics of BigData
  • How Bigdata becomes Problem
  • That is Hadoop
  • History of Hadoop
  • Architecture of Hadoop
  • Data Storage and Analysis
  • Comparison with Other systems
  • RDBMS v/s Hadoop
  • Ecosystem
  • Hdfs
  • The design of hdfs
  • Hdfs concepts
  • Blocks
  • Namenodes and Datanodes
  • HDFS Federation
  • HDFS High-Availability
  • Difference B/w Hadoop 1.x.x and 2.x.x
  • Introduction of Cluster
  • Hadoop Daemom
  • Virtualization
  • Intro of vm ware
  • Vm ware Arch.
  • Types Of vm ware
  • Software Distribution
  • Horton Imports
  • LinuxCommand
  • Hadoop commands
  • Run a map reduce app.
  • Ubuntu installation
  • Installation of Hadoop on real machine.
  • Basic commands
  • JobTracker and TaskTracker
  • Topology Hadoop cluster
  • Example of MapReduce
  • Map Function
  • Reduce Function
  • Java Implementation of MapReduce
  • DataFlow of MapReduce
  • Anatomy of MapReduce Job (MR-1)
  • Submission & Initialization of MapReduce Job (What Happen ?)
  • Assigning & Execution of Tasks
  • Monitoring & Progress of MapReduce Job
  • Completion of Job
  • Handling of MapReduce Job
  • Limitation of Current Architecture (Classic)
  • What are the Requirement ?
  • Introduction of YARN
  • YARN Architecture
  • installing hive
  • the hive shell
  • an ex.
  • Running hive
  • configuring hive
  • hive services
  • comparison with traditional databases
  • schema on read versus schema on write
  • updates, transactions,and indexes
  • hiveQl
  • Data types
  • Operators and functions.
  • tables
  • managed tables and external table
  • partitions and buckets
  • storage formats
  • importing data
  • altering tables
  • dropping tables
  • querying data
  • sorting and aggregating
  • map reduce scripts
  • joins
  • subqueries
  • Partitioning
  • Bucketing
  • Installing and running Pig
  • Execution Types
  • Running Pig Programs
  • Grunt
  • pig latin editors
  • an ex.
  • generating ex.
  • comparison with databases
  • pig latin
  • structure
  • statements
  • expressions
  • types
  • schemas
  • functions
  • macros
  • user-defined func.
  • a filter udf
  • an eval udf
  • a load udf
  • data processing operators
  • loading and storing data
  • filtering data
  • grouping and joining data
  • sorting data
  • combining and splitting data
  • Introduction of Sqoop
  • Installation of Sqoop
  • How Sqoop Works
  • Configuring Bashrc
  • Download and configure Mysql
  • Installation of Mysql
  • Import table
  • Importing into Target Directory
  • Import Subset of table data
  • Alter a table
  • Export a table
  • Installing and running Hbase
  • Basic Commands
  • How to connect Hive With Hbase
  • How to update row
  • How to delete row
  • How to alter table
  • Introduction of Flume
  • Installing flume
  • Architecture
  • Streaming of data
  • Insulate System
  • Data Delivery
  • Scale Horizontally
  • How Flume Works
  • Its Components:-
  • Events
  • Source
  • Sinks
  • Channel
  • Agent
  • Client
  • Live Data Fetching
  • Twitts Data Fetch
  • Introduction to Zookeeper
  • Zookeeper Installation
  • Basic Commands
  • Install multinode cluster
  • Configuration on Multinode
  • Introduction
  • Installation
  • Basic Commands
  • Introduction of Spark
  • Installation of Spark
  • Introduction of Scala
  • Installation of Scala
  • Configure EC2
  • How to make instance
  • Configure instance
  • Excess instance through putty
  • Installation of Hadoop on Cloud
  • Introduction
  • How to install Cassandra
  • Basic Commands
  • Sentiments data analysis using Hadoop
  • Demonetization analysis through pig
  • Working of Hadoop in real word using Sqoop Jobs
  • Data Compression using Hive
  • Comparative analysis Between Hive and Pig.

Big data analytics certification can help you build a better career in big data

Nowadays industries are looking for better ways to pull the information they require from huge volumes of data available to them. Big data system control and transfer large sets of data, administrators store, making them amenable to analysis. Big data analytics certification is the practice of examining the raw data to identify patterns and draw conclusions. Industry intelligence requires the selection and organization of information to report on business activities, often pulling data from those very sets.

With the flow in big data interest comes an increasing number of certifications to recognize the necessary skills in working with enormous data sets. The core audience is an IT expert with a background in data mining, business intelligence, analytics, or data management, along with a knack for and interest in mathematics and statistics. 

Why is Hadoop training online important?

  • Error lenience – it saves data and application processing against hardware failure. When a node sinks, tasks are automatically rechanneled to other nodes to make sure the distributed computing does not fail. It stores multiple copies of all data automatically.
  • Flexibility – Apart from conventional relational databases, You can collect as much data as you want and choose how to use it later. That also covers unstructured data like videos, images, and text.
  • Stores and processes huge amounts of data quickly – With data varieties and quantities continuously enhanced, especially from social media and the internet.
  • Computing power – The allocating computing model of Hadoop processes big data fast. You can get huge processing power if you have huge computing nodes.
  • Costs cut – The open-source framework is free. It utilizes material hardware to mass large quantities of data.

If you are interested in being acquainted with more about Big Data, check out our, big data online course which is designed for working professionals and provides case studies & projects, covers various programming languages & tools, practical hands-on training, rigorous learning & job placement assistance with top firms.

JNtech Networks is the industry-recognized Hadoop training and we properly guide you to your successful future.

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