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Big Data Hadoop Practical Training

  • Big Data Overview
  • How to Process Big Data?
  • What is Hadoop?
  • Features and Elements of Hadoop
  • Uses of Hadoop
  • Hadoop Ecosystem
  • Data Analytics Structure
  • Rdbms Vs Hadoop
  • Installation and Configuration
  • Importance Of Hdfs
  • Hdfs Features
  • Daemons Of Hadoop
    • Name Node
    • Data Node
    • Secondary Name Node
    • Job Tracker
    • Task Tracker
  • Hadoop 2x Configuration Files
  • Data Storage In Hdfs
  • Accessing Hdfs
  • Fault Tolerance
  • Hdfs Federation
  • Understand Hadoop Mapreduce Framework
  • Working With Mapreduce In Hdfs
  • Concepts Of Input Splits, Combiner And Partitioner And Demos In Mapreduce
  • Traditional Wav S Mapreduce Way
  • Why Mapreduce
  • Hadoop 2x Mapreduce Architecture And Components
  • Yarn Workflow
  • How Mapreduce Works
  • Mapreduce Algorithms
  • Writing Mapreduce Program
  • 3mapper And Reducer
  • Input And Output Format In Mapreduce
  • Data Localization
  • Hadoop I/O
  • Understanding Pig
  • Pig Use Cases
  • Pig Vs Mapreduce
  • Pig Scripting And Running Modes
  • Programming Structure In Pig
  • Data Types In Pig
  • Execution Modes In Pig
  • Loading Data And Exploring Pig
  • Latin Commands
  • Learn About Hive Concepts
  • Data Types In Hive
  • Loading And Querying Data In Hive
  • Hive Scripts And Udf
  • Hive Vs Pig
  • Hive Architecture And Components
  • Partitions And Buckets
  • Hive Vs Rdbms
  • Introduction To Hbase
  • Advanced Hive Concepts
  • Hbase Architecture And Design
  • Hbase Vs Rdbms
  • Read And Write Pipeline
  • Hbase Commands
  • Hbase Shell
  • Client Api And Data Loading Techniques
  • Zoopkeeper Service
  • Hadoop Integration
  • Introduction to Talend
  • Loading Data From Rdbms Into Hdfs Using Sqoop and Talend
  • Managing Real Time Data Using Flume
  • Other Important Data Analysis Features with Hadoop Elements
  • Solving Big Data Issues
  • Discussing Data Sets
  • Live Training Under Expert Supervision
  • Module Wise Assignments

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Please Find The Big Data Hadoop Training Course Duration.

Course Course Duration
Big Data Hadoop 50 – 60 Hours



What is the difference between Hadoop and Traditional RDBMS?

Hadoop and RDBMS two different types of data management tools used by individuals and business Enterprises. Hadoop is an open source Framework specifically designed for storing and processing a large amount of data in an accurate manner while RDBMS is a Relational database management system used for transactional system and reporting and archiving. Hadoop is very scalable and high-performance data management application mainly used for storing a large amount of data in the file system of multiple computers. Whereas RDBMS stores data of related format and helps the user to manipulate and update the data as necessary. It is also used to represent the data in graphical or tabular appearance for better readability.

On what concept the Hadoop framework works?

Hadoop Framework actually works on the principle of MapReduce. On the map section, the input problem would be broken into several small data crunches, which are then distributed to its working notes for further processing. The data is then accepted and processed independently by these working notes and later provided to the namenode.

In reduce section the result of the data processing are collected from the working nodes in order to prepare the final output. Hadoop Framework also works on the concept of SIMD (single instruction multiple data) and HDFS (Hadoop distributed file system).

What is Hadoop streaming?

Hadoop streaming refers to the utility which allows the user to create and run map reduce tasks with any script or executable mapper or reducer. Hadoop streaming comes with Hadoop distribution and utilized widely to execute programs for big data analysis. It can be performed using programming languages such as Unix, Java, Python, PHP etc. Hadoop streaming is similar to pipe operation in Linux and allows the user to transfer data at a defined frame rate and on a continuous stream. It is mainly utilized to handle continuous streaming of online videos.

Differentiate between Structured and Unstructured data.

Structured data refers to the kind of data which is well formatted and processed and easy to understand for the Machines. It is the representation of the essence of same information derived for the purpose. Structured data is simple and straight forward for search engine algorithm and other operations. Whereas unstructured data is the kind of data that has been derived from different sources without any changes or sorting. It doesn’t fit in relational databases and it is not possible to perform algorithms on such data. Emails and social media data are some of the examples of unstructured data.

What are the main components of a Hadoop Application?

There are several different components of Hadoop application which are as follows:

Hadoop common: Base API for all the Hadoop components.

HDFC (Hadoop distributed file system): The primary storage system of Hadoop, which stores and process big data.

YARN (yet another resource negotiator): A processing framework in Hadoop that offers resource management and enables multiple data processing engine.

 MapReduce – Distributed data processing framework of Apache Hadoop: Assist in processing large structured and unstructured data stored in HDFS.

What is the best hardware configuration to run Hadoop?

Similar to any other application, Hadoop also requires a specific hardware configuration to run on a system. In order to run Hadoop application on your computer or laptop, you need to have Dual Core processor with at least 4 GB of RAM that uses ECC memory. It is important to have ECC memory to prevent the user from any checksum error. However, it is recommended to use and Core I series processor along with at least 160 GB hard drive. You are also required to have an Internet connection with at least 1 MBPS speed. As for the operating system, it is highly recommended to install Hadoop application in Red Hat Enterprise Linux, Ubuntu or centOS.


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