This document discusses the features, key advantages, and versions of Hadoop. Hadoop is an open-source framework that allows for the distributed processing of large data sets across clusters of computers. It offers tooling, code generation, modeling, and scheduling features that help developers build and run big data applications efficiently. Some key advantages of Hadoop include its scalability, cost effectiveness, flexibility to handle various data types, fast processing, and resilience to failures. The document outlines the differences between Hadoop versions 1.0 and 2.0, with 2.0 offering enhanced flexibility and support for various data processing engines beyond just MapReduce.
AN OVERVIEW OF BIGDATA AND HADOOP . THE ARCHITECHTURE IT USES AND THE WAY IT WORKS ON THE DATA SETS. THE SIDES ALSO SHOW THE VARIOUS FIELDS WHERE THEY ARE MOSTLY USED AND IMPLIMENTED
AN OVERVIEW OF BIGDATA AND HADOOP . THE ARCHITECHTURE IT USES AND THE WAY IT WORKS ON THE DATA SETS. THE SIDES ALSO SHOW THE VARIOUS FIELDS WHERE THEY ARE MOSTLY USED AND IMPLIMENTED
Presented By :- Rahul Sharma
B-Tech (Cloud Technology & Information Security)
2nd Year 4th Sem.
Poornima University (I.Nurture),Jaipur
www.facebook.com/rahulsharmarh18
This presentation discusses the following topics:
Hadoop Distributed File System (HDFS)
How does HDFS work?
HDFS Architecture
Features of HDFS
Benefits of using HDFS
Examples: Target Marketing
HDFS data replication
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using a simple programming model. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-avaiability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-availabile service on top of a cluster of computers, each of which may be prone to failures.
Big Data raises challenges about how to process such vast pool of raw data and how to aggregate value to our lives. For addressing these demands an ecosystem of tools named Hadoop was conceived.
Hadoop vs Spark | Which One to Choose? | Hadoop Training | Spark Training | E...Edureka!
( Apache Spark Training: https://www.edureka.co/apache-spark-scala-training )
( Hadoop Training: https://www.edureka.co/hadoop )
This Edureka Hadoop vs Spark video will help you to understand the differences between Hadoop and Spark. We will be comparing them on various parameters. We will be taking a broader look at:
1. Introduction to Hadoop
2. Introduction to Apache Spark
3. Spark vs Hadoop -
Performance
Ease of Use
Cost
Data Processing
Fault tolerance
Security
4. Hadoop Use-cases
5. Spark Use-cases
The critical thing to remember about Spark and Hadoop is they are not mutually exclusive or inclusive but they work well together and makes the combination strong enough for lots of big data applications.
Hi all, its presentation about the big data analysis done using a data mining tool known as HADOOP, which is based on Distributive file system and uses parallel computing for working.
Apache Hive is a tool built on top of Hadoop for analyzing large, unstructured data sets using a SQL-like syntax, thus making Hadoop accessible to legions of existing BI and corporate analytics researchers.
Presented By :- Rahul Sharma
B-Tech (Cloud Technology & Information Security)
2nd Year 4th Sem.
Poornima University (I.Nurture),Jaipur
www.facebook.com/rahulsharmarh18
This presentation discusses the following topics:
Hadoop Distributed File System (HDFS)
How does HDFS work?
HDFS Architecture
Features of HDFS
Benefits of using HDFS
Examples: Target Marketing
HDFS data replication
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using a simple programming model. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-avaiability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-availabile service on top of a cluster of computers, each of which may be prone to failures.
Big Data raises challenges about how to process such vast pool of raw data and how to aggregate value to our lives. For addressing these demands an ecosystem of tools named Hadoop was conceived.
Hadoop vs Spark | Which One to Choose? | Hadoop Training | Spark Training | E...Edureka!
( Apache Spark Training: https://www.edureka.co/apache-spark-scala-training )
( Hadoop Training: https://www.edureka.co/hadoop )
This Edureka Hadoop vs Spark video will help you to understand the differences between Hadoop and Spark. We will be comparing them on various parameters. We will be taking a broader look at:
1. Introduction to Hadoop
2. Introduction to Apache Spark
3. Spark vs Hadoop -
Performance
Ease of Use
Cost
Data Processing
Fault tolerance
Security
4. Hadoop Use-cases
5. Spark Use-cases
The critical thing to remember about Spark and Hadoop is they are not mutually exclusive or inclusive but they work well together and makes the combination strong enough for lots of big data applications.
Hi all, its presentation about the big data analysis done using a data mining tool known as HADOOP, which is based on Distributive file system and uses parallel computing for working.
Apache Hive is a tool built on top of Hadoop for analyzing large, unstructured data sets using a SQL-like syntax, thus making Hadoop accessible to legions of existing BI and corporate analytics researchers.
Enroll Free Live demo of Hadoop online training and big data analytics courses online and become certified data analyst/ Hadoop developer. Get online Hadoop training & certification.
Enroll Free Live demo of Hadoop online training and big data analytics courses online and become certified data analyst/ Hadoop developer. Get online Hadoop training & certification.
This presentation provides a comprehensive introduction to the Hadoop Distributed System, a powerful and widely used framework for distributed storage and processing of large-scale data. Hadoop has revolutionized the way organizations manage and analyze data, making it a crucial tool in the field of big data and data analytics.
In this presentation, we explore the key components and features of Hadoop, shedding light on the fundamental building blocks that enable its exceptional data processing capabilities. We cover essential topics, including the Hadoop Distributed File System (HDFS), MapReduce, YARN (Yet Another Resource Negotiator), and Hadoop Ecosystem components like Hive, Pig, and Spark.
M. Florence Dayana - Hadoop Foundation for Analytics.pptxDr.Florence Dayana
Hadoop Foundation for Analytics
History of Hadoop
Features of Hadoop
Key Advantages of Hadoop
Why Hadoop
Versions of Hadoop
Eco Projects
Essential of Hadoop ecosystem
RDBMS versus Hadoop
Key Aspects of Hadoop
Components of Hadoop
Overview of Big data, Hadoop and Microsoft BI - version1Thanh Nguyen
Big Data and advanced analytics are critical topics for executives today. But many still aren't sure how to turn that promise into value. This presentation provides an overview of 16 examples and use cases that lay out the different ways companies have approached the issue and found value: everything from pricing flexibility to customer preference management to credit risk analysis to fraud protection and discount targeting. For the latest on Big Data & Advanced Analytics: http://mckinseyonmarketingandsales.com/topics/big-data
Overview of big data & hadoop version 1 - Tony NguyenThanh Nguyen
Overview of Big data, Hadoop and Microsoft BI - version1
Big Data and Hadoop are emerging topics in data warehousing for many executives, BI practices and technologists today. However, many people still aren't sure how Big Data and existing Data warehouse can be married and turn that promise into value. This presentation provides an overview of Big Data technology and how Big Data can fit to the current BI/data warehousing context.
http://www.quantumit.com.au
http://www.evisional.com
Presentation regarding big data. The presentation also contains basics regarding Hadoop and Hadoop components along with their architecture. Contents of the PPT are
1. Understanding Big Data
2. Understanding Hadoop & It’s Components
3. Components of Hadoop Ecosystem
4. Data Storage Component of Hadoop
5. Data Processing Component of Hadoop
6. Data Access Component of Hadoop
7. Data Management Component of Hadoop
8.Hadoop Security Management Tool: Knox ,Ranger
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
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An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
3. 1. Tooling :
Developers can create, design, and deploy big
data services on any platform or development
environment as per their choice.
4. 2. Code generation :
Hadoop big data suite, there is no need of
writing, debugging, analyzing, and optimizing
MapReduce code
the complete code is auto generated.
5. 3. Modeling :
Every Hadoop distribution provides the
infrastructure to integrate Hadoop clusters.
developers have to make complex codes to
develop MapReduce program.
They can write such codes in simple Java, or
even can use optimized languages, such as
PigLatin, HQL,etc.
6. 4. Scheduling :
Big Data jobs execution needs to be monitored
and scheduled.
Instead of writing jobs for scheduling, developers
can take help of big data suite to define and
handle the execution tasks in most efficient way.
7. 5. Integration :
Hadoop- it wants to integrate data from all types
of products and technologies.
Along with files and SQL databases, developers
wants to integrate data from NoSQL databases,
social media, B2B products, etc.
8. Key Advantages
There are many advantages associated with Hadoop.
In this presentation we have came up with some
major advantages of Hadoop.
9. Scalable:
Hadoop is highly scalable.
it can store and distribute very large data sets
across hundreds of inexpensive servers.
10. Cost effective:
Owing to its scale-out architecture
Hadoop offers a cost effective storage solution
and processing
11. Flexible:
Ability to work with all kind of data: structured,
semi-structured and unstructured.
it can be used for a wide variety of purposes,
such as log processing, recommendation
systems,data warehousing ,data mining and more.
12. Fast:
the process is extremely fast in compared to other
conventional systems owing to the ”move code to
data” paradigm.
13. Resilient to failure:
Hadoop is fault tolerance.
It practices replication of data diligently.
ensuring that in the event of a node failure.
15. There are two version of Hadoop available:
1.Hadoop 1.0
2.Hadoop 2.0
16. Hadoop 1.0
It has two main parts:
1.Data storage framework
2.Data processing framework
1.Data storage framework:
It is a general –purpose filesystem called
Hadoop Distributed File System.
HDFS is schema-less.
It stores data files can be in just about any
format.
17. 2.Data processing framework:
Is a simple functional programming model.
It essentially uses two functions:
1.MAP
2.REDUCE
1.The “Mapers” take set of key-value pairs and
generate intermediate data.
2.The“Reducers” then act on this input to
produce the output data.
19. Hadoop 2.0
HDFS continues to be the data storage
framework.
A new and separate resource management
framework called Yet Another Resource
Negotiator(YARN) has been added.
Any application capable of dividing itself into
parallel tasks is supported by YARN.
YARN coordinates the allocation of subtasks of the
submitted applications.
20. Further enhancing the flexibility , scalability , and
efficiency of the applications.
ApplicationMaster is able to run any application
and not just MapReduce.
only supports batch processing but also real-time
processing.
MapReduce is no longer the only data
processing option.