Big Data is a term defined for data sets that are extreme and complex where traditional data processing applications are inadequate to deal with them. The term Big Data often refers simply to the use of predictive investigation on analytic methods that extract value from data. Big data is generalized as a large data which is a collection of big datasets that cannot be processed using traditional computing techniques. Big data is not purely a data, rather than it is a complete subject involves various tools, techniques and frameworks. Big data can be any structured collection which results incapability of conventional data management methods. Hadoop is a distributed example used to change the large amount of data. This manipulation contains not only storage as well as processing on the data. Hadoop is an open- source software framework for dispersed storage and processing of big data sets on computer clusters built from commodity hardware. HDFS was built to support high throughput, streaming reads and writes of extremely large files. Hadoop Map Reduce is a software framework for easily writing applications which process vast amounts of data. Wordcount example reads text files and counts how often words occur. The input is text files and the result is wordcount file, each line of which contains a word and the count of how often it occurred separated by a tab.
Survey of Parallel Data Processing in Context with MapReduce cscpconf
MapReduce is a parallel programming model and an associated implementation introduced by
Google. In the programming model, a user specifies the computation by two functions, Map and Reduce. The underlying MapReduce library automatically parallelizes the computation, and handles complicated issues like data distribution, load balancing and fault tolerance. The original MapReduce implementation by Google, as well as its open-source counterpart,Hadoop, is aimed for parallelizing computing in large clusters of commodity machines.This paper gives an overview of MapReduce programming model and its applications. The author has described here the workflow of MapReduce process. Some important issues, like fault tolerance, are studied in more detail. Even the illustration of working of Map Reduce is given. The data locality issue in heterogeneous environments can noticeably reduce the Map Reduce performance. In this paper, the author has addressed the illustration of data across nodes in a way that each node has a balanced data processing load stored in a parallel manner. Given a data intensive application running on a Hadoop Map Reduce cluster, the auhor has exemplified how data placement is done in Hadoop architecture and the role of Map Reduce in the Hadoop Architecture. The amount of data stored in each node to achieve improved data-processing performance is explained here.
Asserting that Big Data is vital to business is an understatement. Organizations have generated more and more data for years, but struggle to use it effectively. Clearly Big Data has more important uses than ensuring compliance with regulatory requirements. In addition, data is being generated with greater velocity, due to the advent of new pervasive devices (e.g., smartphones, tablets, etc.), social Web sites (e.g., Facebook, Twitter, LinkedIn, etc.) and other sources like GPS, Google Maps, heat/pressure sensors, etc.
One of the challenges in storing and processing the data and using the latest internet technologies has resulted in large volumes of data. The technique to manage this massive amount of data and to pull out the value, out of this volume is collectively called Big data. Over the recent years, there has been a rising interest in big data for social media analysis. Online social media have become the important platform across the world to share information. Facebook, one of the largest social media site receives posts in millions every day. One of the efficient technologies that deal with the Big Data is Hadoop. Hadoop, for processing large data volume jobs uses MapReduce programming model. This paper provides a survey on Hadoop and its role in facebook and a brief introduction to HIVE.
Large amount of data are produced daily from various fields such as science, economics,
engineering and health. The main challenge of pervasive computing is to store and analyze large amount of
data.This has led to the need for usable and scalable data applications and storage clusters. In this article, we
examine the hadoop architecture developed to deal with these problems. The Hadoop architecture consists of
the Hadoop Distributed File System (HDFS) and Mapreduce programming model, which enables storage and
computation on a set of commodity computers. In this study, a Hadoop cluster consisting of four nodes was
created.Regarding the data size and cluster size, Pi and Grep MapReduce applications, which show the effect of
different data sizes and number of nodes in the cluster, have been made and their results examined.
Today’s era is generally treated as the era of data on each and every field of computing application huge amount of data is generated. The society is gradually more dependent on computers so large amount of data is generated in each and every second which is either in structured format, unstructured format or semi structured format. These huge amount of data are generally treated as big data. To analyze big data is a biggest challenge in current world. Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage and it generally follows horizontal processing. Map Reduce programming is generally run over Hadoop Framework and process the large amount of structured and unstructured data. This Paper describes about different joining strategies used in Map reduce programming to combine the data of two files in Hadoop Framework and also discusses the skewness problem associate to it.
Survey of Parallel Data Processing in Context with MapReduce cscpconf
MapReduce is a parallel programming model and an associated implementation introduced by
Google. In the programming model, a user specifies the computation by two functions, Map and Reduce. The underlying MapReduce library automatically parallelizes the computation, and handles complicated issues like data distribution, load balancing and fault tolerance. The original MapReduce implementation by Google, as well as its open-source counterpart,Hadoop, is aimed for parallelizing computing in large clusters of commodity machines.This paper gives an overview of MapReduce programming model and its applications. The author has described here the workflow of MapReduce process. Some important issues, like fault tolerance, are studied in more detail. Even the illustration of working of Map Reduce is given. The data locality issue in heterogeneous environments can noticeably reduce the Map Reduce performance. In this paper, the author has addressed the illustration of data across nodes in a way that each node has a balanced data processing load stored in a parallel manner. Given a data intensive application running on a Hadoop Map Reduce cluster, the auhor has exemplified how data placement is done in Hadoop architecture and the role of Map Reduce in the Hadoop Architecture. The amount of data stored in each node to achieve improved data-processing performance is explained here.
Asserting that Big Data is vital to business is an understatement. Organizations have generated more and more data for years, but struggle to use it effectively. Clearly Big Data has more important uses than ensuring compliance with regulatory requirements. In addition, data is being generated with greater velocity, due to the advent of new pervasive devices (e.g., smartphones, tablets, etc.), social Web sites (e.g., Facebook, Twitter, LinkedIn, etc.) and other sources like GPS, Google Maps, heat/pressure sensors, etc.
One of the challenges in storing and processing the data and using the latest internet technologies has resulted in large volumes of data. The technique to manage this massive amount of data and to pull out the value, out of this volume is collectively called Big data. Over the recent years, there has been a rising interest in big data for social media analysis. Online social media have become the important platform across the world to share information. Facebook, one of the largest social media site receives posts in millions every day. One of the efficient technologies that deal with the Big Data is Hadoop. Hadoop, for processing large data volume jobs uses MapReduce programming model. This paper provides a survey on Hadoop and its role in facebook and a brief introduction to HIVE.
Large amount of data are produced daily from various fields such as science, economics,
engineering and health. The main challenge of pervasive computing is to store and analyze large amount of
data.This has led to the need for usable and scalable data applications and storage clusters. In this article, we
examine the hadoop architecture developed to deal with these problems. The Hadoop architecture consists of
the Hadoop Distributed File System (HDFS) and Mapreduce programming model, which enables storage and
computation on a set of commodity computers. In this study, a Hadoop cluster consisting of four nodes was
created.Regarding the data size and cluster size, Pi and Grep MapReduce applications, which show the effect of
different data sizes and number of nodes in the cluster, have been made and their results examined.
Today’s era is generally treated as the era of data on each and every field of computing application huge amount of data is generated. The society is gradually more dependent on computers so large amount of data is generated in each and every second which is either in structured format, unstructured format or semi structured format. These huge amount of data are generally treated as big data. To analyze big data is a biggest challenge in current world. Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage and it generally follows horizontal processing. Map Reduce programming is generally run over Hadoop Framework and process the large amount of structured and unstructured data. This Paper describes about different joining strategies used in Map reduce programming to combine the data of two files in Hadoop Framework and also discusses the skewness problem associate to it.
The data management industry has matured over the last three decades, primarily based on relational database management system(RDBMS) technology. Since the amount of data collected, and analyzed in enterprises has increased several folds in volume, variety and velocityof generation and consumption, organisations have started struggling with architectural limitations of traditional RDBMS architecture. As a result a new class of systems had to be designed and implemented, giving rise to the new phenomenon of “Big Data”. In this paper we will trace the origin of new class of system called Hadoop to handle Big data.
Infrastructure Considerations for Analytical WorkloadsCognizant
Using Apache Hadoop clusters and Mahout for analyzing big data workloads yields extraordinary performance; we offer a detailed comparison of running Hadoop in a physical vs. virtual infrastructure environment.
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
Harnessing Hadoop: Understanding the Big Data Processing Options for Optimizi...Cognizant
A guide to using Apache Hadoop as your open source big data platform of choice, including the vendors that make various Hadoop flavors, related open source tools, Hadoop capabilities and suitable applications.
Implementation of Multi-node Clusters in Column Oriented Database using HDFSIJEACS
Generally HBASE is NoSQL database which runs in the Hadoop environment, so it can be called as Hadoop Database. By using Hadoop distributed file system and map reduce with the implementation of key/value store as real time data access combines the deep capabilities and efficiency of map reduce. Basically testing is done by using single node clustering which improved the performance of query when compared to SQL, even though performance is enhanced, the data retrieval becomes complicated as there is no multi node clusters and totally based on SQL queries. In this paper, we use the concepts of HBase, which is a column oriented database and it is on the top of HDFS (Hadoop distributed file system) along with multi node clustering which increases the performance. HBase is key/value store which is Consistent, Distributed, Multidimensional and Sorted map. Data storage in HBase in the form of cells, and here those cells are grouped by a row key. Hence our proposal yields better results regarding query performance and data retrieval compared to existing approaches.
Big Data is an evolution of Business Intelligence (BI).
Whereas traditional BI relies on data warehouses limited in size
(some terabytes) and it hardly manages unstructured data and
real-time analysis, the era of Big Data opens up a new technological
period offering advanced architectures and infrastructures
allowing sophisticated analyzes taking into account these new
data integrated into the ecosystem of the business . In this article,
we will present the results of an experimental study on the performance
of the best framework of Big Analytics (Spark) with the
most popular databases of NoSQL MongoDB and Hadoop. The
objective of this study is to determine the software combination
that allows sophisticated analysis in real time.
Vikram Andem Big Data Strategy @ IATA Technology Roadmap IT Strategy Group
Vikram Andem, Senior Manager, United Airlines, A case for Bigdata Program and Strategy @ IATA Technology Roadmap 2014, October 13th, 2014, Montréal, Canada
Examining the Usability of Message Reading Features on Smartwatchesinventionjournals
This study examined smart watch message reading features by considering the following assessment points: simplicity, comprehensibility, perceived usefulness, and overall satisfaction. Three smart watches (Pebble Watch, Samsung Galaxy Gear2, and Sony Smartwatch2) were subjected to user tests, and indepth user interviews were conducted. The results showed that 83.3% of participants experienced difficulty in checking and reading messages. Screen size, text size, and accessing messages were the top three factors causing inconvenience. The participants believed that the most useful feature of the smart watch was message notification, and message reading was also considered useful. The message notification function could be further developed for smartwatch design.
la naturaleza es un fuente de vida para todos los seres vivos por que sin ella no seriamos nadie y ya estuviéramos muertos por so hay que cuidarla bien no quemar basura y no contaminar los ríos para hacer un planeta de bien.
The data management industry has matured over the last three decades, primarily based on relational database management system(RDBMS) technology. Since the amount of data collected, and analyzed in enterprises has increased several folds in volume, variety and velocityof generation and consumption, organisations have started struggling with architectural limitations of traditional RDBMS architecture. As a result a new class of systems had to be designed and implemented, giving rise to the new phenomenon of “Big Data”. In this paper we will trace the origin of new class of system called Hadoop to handle Big data.
Infrastructure Considerations for Analytical WorkloadsCognizant
Using Apache Hadoop clusters and Mahout for analyzing big data workloads yields extraordinary performance; we offer a detailed comparison of running Hadoop in a physical vs. virtual infrastructure environment.
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
Harnessing Hadoop: Understanding the Big Data Processing Options for Optimizi...Cognizant
A guide to using Apache Hadoop as your open source big data platform of choice, including the vendors that make various Hadoop flavors, related open source tools, Hadoop capabilities and suitable applications.
Implementation of Multi-node Clusters in Column Oriented Database using HDFSIJEACS
Generally HBASE is NoSQL database which runs in the Hadoop environment, so it can be called as Hadoop Database. By using Hadoop distributed file system and map reduce with the implementation of key/value store as real time data access combines the deep capabilities and efficiency of map reduce. Basically testing is done by using single node clustering which improved the performance of query when compared to SQL, even though performance is enhanced, the data retrieval becomes complicated as there is no multi node clusters and totally based on SQL queries. In this paper, we use the concepts of HBase, which is a column oriented database and it is on the top of HDFS (Hadoop distributed file system) along with multi node clustering which increases the performance. HBase is key/value store which is Consistent, Distributed, Multidimensional and Sorted map. Data storage in HBase in the form of cells, and here those cells are grouped by a row key. Hence our proposal yields better results regarding query performance and data retrieval compared to existing approaches.
Big Data is an evolution of Business Intelligence (BI).
Whereas traditional BI relies on data warehouses limited in size
(some terabytes) and it hardly manages unstructured data and
real-time analysis, the era of Big Data opens up a new technological
period offering advanced architectures and infrastructures
allowing sophisticated analyzes taking into account these new
data integrated into the ecosystem of the business . In this article,
we will present the results of an experimental study on the performance
of the best framework of Big Analytics (Spark) with the
most popular databases of NoSQL MongoDB and Hadoop. The
objective of this study is to determine the software combination
that allows sophisticated analysis in real time.
Vikram Andem Big Data Strategy @ IATA Technology Roadmap IT Strategy Group
Vikram Andem, Senior Manager, United Airlines, A case for Bigdata Program and Strategy @ IATA Technology Roadmap 2014, October 13th, 2014, Montréal, Canada
Examining the Usability of Message Reading Features on Smartwatchesinventionjournals
This study examined smart watch message reading features by considering the following assessment points: simplicity, comprehensibility, perceived usefulness, and overall satisfaction. Three smart watches (Pebble Watch, Samsung Galaxy Gear2, and Sony Smartwatch2) were subjected to user tests, and indepth user interviews were conducted. The results showed that 83.3% of participants experienced difficulty in checking and reading messages. Screen size, text size, and accessing messages were the top three factors causing inconvenience. The participants believed that the most useful feature of the smart watch was message notification, and message reading was also considered useful. The message notification function could be further developed for smartwatch design.
la naturaleza es un fuente de vida para todos los seres vivos por que sin ella no seriamos nadie y ya estuviéramos muertos por so hay que cuidarla bien no quemar basura y no contaminar los ríos para hacer un planeta de bien.
Taller ErgoCV en el XII Congreso Internacional de Prevención de Riesgos Labor...ErgoCV
Desde ErgoCV se han realizado a lo largo del ejercicio 2013 talleres de trabajo, entre otras materias sobre ergonomía en inclusión de personas con discapacidad, procedimientos de violencia en el trabajo, y retos de la ergonomia en la internacionalización de las Empresas.
En dichos talleres han participado empresas, técnicos de prevención y departamentos de recursos humanos.
En el ORP Zaragoza se presentaron las conclusiones de esos talleres, completándose con las aportaciones de otros participantes durante el taller de PERU, ECUADOR, VENEZUELA, CHILE y ARGENTINA.
Today we know that the propensity to crime and violence is not only the effect of morphological, psychological and social factors, as believed Lombroso, Garofalo and Ferri, influential members of the Positive School in Italy. The tendency to crime is the product of many components, also biological and genetic roots. To say this thing is Adrian Raine, an eminent psychiatrist and criminologist English, who seeks to recover and reinterpret Lombroso, but especially opens a whole new frontier: the neurocriminology. The idea was to try the basics of anti-social behavior in the folds of the brain, using neuroscience and high tech, until you get to predict the propensity to crime, crossing genetic parameters, biological, but also social. We are currently faced, therefore, with a dilemma that is not easy to solve. It is impossible to think that man’s actions are completely dominated by the fatality of destiny ˗ and that, therefore, the offender is an irresponsible ˗ but it is no longer possible refer to the good old concept of “free will” and believe that he is a “subject” in a strong sense, completely free to choose between good and evil. The presence in the courts of neuroscientists and the individualization of punitive sanctions: these are now the most urgent problems that arise in the field of criminal law.
In Causality Principle as the Framework to Contextualize Time in Modern Physicsinventionjournals
Since the moment Boethius meditated on the nature of time in his fifth book on The Consolation of Philosophy, we have more tools to reflect on the subject. The onset of relativity and quantum physics provides us with the best insight, to date, that guides our reflections on the philosophical debates that attempt to theorize a definition of time. To clearly address the problems related to the theoretical models that account for the nature of time, adjustments to our interpretation of the contextual issues involved in special relativity are in order if we are going to preserve our notion of causal reality. As the construction of string theory emerges as the reigning theory for quantum gravity, a precise picture of causal reality can be accounted for through theories such as Dyson’s Chronological Protection Agency, Hořava’s theory of gravity, and new insight to how simultaneity is interpreted in relativity theory. With this model, the question about time in the philosophical debates can now be clearly defined through the Presentists’ view of the universe. Thus, if we are going to accept the premise of quantum mechanics (QM) and the theory of relativity, we can safely say that string theory (ST) is a reasonable theory of quantum gravity and that its conclusions about time must be taken seriously.
Environment In Context : A Perspective From Environment Behavior Relationinventionjournals
Researchers in a wide variety of fields – study of environment - behaviour relations – have increasingly emphasized the role context in human functioning. It may be worth-while for those of us interested in environment behaviour research to review some systematic approaches to context as a means for identifying new research problems and for advancing our theoretical perspectives ,which may have practical implications for improving the functioning of human beings in their everyday life environments through environmental education .Socio-ecological models contribute to the understanding of how context influences human development and construction of worldviews. However, the claim that socio-ecological models represent the “true” influencers of an individual might be a misrepresentation of the complexity of whole eco-logical systems. This article explores the possibility of adapting the use of the “socio-ecological model” to better represent the ecological influencers, rather than the primary focus of human and social factors.
“Desquamative Gingivitis Treated By An Antioxidant Therapy- A Case Report”inventionjournals
Desquamative gingivitis is described as an erythematous, desquamated or eroded gingival lesion. Various etiologic factors are present for the appearance of such lesions. Despite of considering etiology, treatment is oftenly provided by systemic or topical corticosteroids. Apart from steroid application, another optionable treatment is antioxidant therapy which provides rapid healing of the tissue. As antioxidants posses various advantageous properties, it can be considered as a first treatment option for desquamative gingivitis. The presented case report of desquamative gingivitis is successfully treated using systemic antioxidants in the form of commercially available „oxitard capsule‟.
Entrepreneurship is considered as a source of wealth creation, economic growth, social progress, and technological development. The current paper seeks to shed light on obstacles that are impeding business creators to start their businesses. To identify the observed constraints, we developed a questionnaire that we addressed to 120 new entrepreneurs drawing on the theoretical and empirical literature. The application of factor analysis has revealed that business creators’ decision to launch their ventures is hindered mainly by the following factors: lack of managerial and business skills, poor training programs, and risk aversion. The study also suggests some recommendations to alleviate obstacles facing new entrepreneurs when deciding to launch their projects
Design Issues and Challenges of Peer-to-Peer Video on Demand System cscpconf
P2P media streaming and file downloading is most popular applications over the Internet.
These systems reduce the server load and provide a scalable content distribution. P2P
networking is a new paradigm to build distributed applications. It describes the design
requirements for P2P media streaming, live and Video on demand system comparison based on their system architecture. In this paper we described and studied the traditional approaches for P2P streaming systems, design issues, challenges, and current approaches for providing P2P VoD services.
Survey on Performance of Hadoop Map reduce Optimization Methodspaperpublications3
Abstract: Hadoop is a open source software framework for storage and processing large scale of datasets on clusters of commodity hardware. Hadoop provides a reliable shared storage and analysis system, here storage provided by HDFS and analysis provided by MapReduce. MapReduce frameworks are foraying into the domain of high performance of computing with stringent non-functional requirements namely execution times and throughputs. MapReduce provides simple programming interfaces with two functions: map and reduce. The functions can be automatically executed in parallel on a cluster without requiring any intervention from the programmer. Moreover, MapReduce offers other benefits, including load balancing, high scalability, and fault tolerance. The challenge is that when we consider the data is dynamically and continuously produced, from different geographical locations. For dynamically generated data, an efficient algorithm is desired, for timely guiding the transfer of data into the cloud over time for geo-dispersed data sets, there is need to select the best data center to aggregate all data onto given that a MapReduce like framework is most efficient when data to be processed are all in one place, and not across data centers due to the enormous overhead of inter-data center data moving in the stage of shuffle and reduce. Recently, many researchers tend to implement and deploy data-intensive and/or computation-intensive algorithms on MapReduce parallel computing framework for high processing efficiency.
There is a growing trend of applications that ought to handle huge information. However, analysing huge information may be a terribly difficult drawback nowadays. For such data many techniques can be considered. The technologies like Grid Computing, Volunteering Computing, and RDBMS can be considered as potential techniques to handle such data. We have a still in growing phase Hadoop Tool to handle such data also. We will do a survey on all this techniques to find a potential technique to manage and work with Big Data.
Big data is data that, by virtue of its velocity, volume, or variety (the three Vs), cannot be easily stored or analyzed with traditional methods. Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware.
Leveraging Map Reduce With Hadoop for Weather Data Analytics iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Mankind has stored more than 295 billion gigabytes (or 295 Exabyte) of data since 1986, as per a report by the University of Southern California. Storing and monitoring this data in widely distributed environments for 24/7 is a huge task for global service organizations. These datasets require high processing power which can’t be offered by traditional databases as they are stored in an unstructured format. Although one can use Map Reduce paradigm to solve this problem using java based Hadoop, it cannot provide us with maximum functionality. Drawbacks can be overcome using Hadoop-streaming techniques that allow users to define non-java executable for processing this datasets. This paper proposes a THESAURUS model which allows a faster and easier version of business analysis.
A short overview of Bigdata along with its popularity, ups and downs from past to present. We had a look of its needs, challenges and risks too. Architectures involved in it. Vendors associated with it.
Similar to Introduction to Big Data and Hadoop using Local Standalone Mode (20)
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Introduction to Big Data and Hadoop using Local Standalone Mode
1. International Journal of Engineering Science Invention
ISSN (Online): 2319 – 6734, ISSN (Print): 2319 – 6726
www.ijesi.org ||Volume 5 Issue 9|| September 2016 || PP. 82-86
www.ijesi.org 82 | Page
Introduction to Big Data and Hadoop using
Local Standalone Mode
K.Srikanth 1
, P.Venkateswarlu 2
, R.Roje Spandana3
1,2,3
(Information Technology, Jawaharlal Nehru Technological University Kakinada, India)
Abstract: Big Data is a term defined for data sets that are extreme and complex where traditional data
processing applications are inadequate to deal with them. The term Big Data often refers simply to the use of
predictive investigation on analytic methods that extract value from data. Big data is generalized as a large
data which is a collection of big datasets that cannot be processed using traditional computing techniques. Big
data is not purely a data, rather than it is a complete subject involves various tools, techniques and frameworks.
Big data can be any structured collection which results incapability of conventional data management methods.
Hadoop is a distributed example used to change the large amount of data. This manipulation contains not only
storage as well as processing on the data. Hadoop is an open- source software framework for dispersed storage
and processing of big data sets on computer clusters built from commodity hardware. HDFS was built to
support high throughput, streaming reads and writes of extremely large files. Hadoop Map Reduce is a software
framework for easily writing applications which process vast amounts of data. Wordcount example reads text
files and counts how often words occur. The input is text files and the result is wordcount file, each line of which
contains a word and the count of how often it occurred separated by a tab.
Keywords: Big Data, Hadoop, HDFS, Map Reduce, WordCount
I. Introduction
Big data is generalized as a large data which is a collection of it is a collection of big datasets [6] that cannot be
processed using traditional computing techniques. Large data is not purely a data, rather than it is a complete
subject involves various techniques, tools and frameworks. Due to the onset of new advanced technologies,
creation, and communication means like media, social networking sites, the amount of data produced by
mankind is growing speedily every year [2]. The large amount of data beginning of time till 2003 was 6 billion
gigabyte. Hadoop is a distributed exemplar used to change the large amount of data. This direction contains not
only storage as well as processing on the data. In addition to simple storage, Google File System was built to
data-intensive, support large-scale, distributed processing applications. The coming year, new paper, related
Map Reduce Simplified Data Processing on Large Clusters, was presented, defining a programming model
framework that provided automatic parallelization and the scale to process thousands of terabytes of big data in
a one job no limitation thousands of machines. When paired, these two systems could be used to build large data
processing [5] clusters on relatively inexpensive, commodity machines. This paper directly inspired the
development of HDFS and Hadoop Map Reduce, respectively. Within the Apache Software Foundation, real
time projects that explicitly make use of integrate with, Hadoop are springing up regularly. Some of these real
time projects make authoring Map Reduce work easier and more jobs accessible, while others focus on getting
data in and out of HDFS, simplify operations, enable deployment in cloud environments, and so on.
II. Big Data
Big Data is a term defined for data sets that are extreme and complex where traditional data processing
applications are inadequate to deal with them. The data is large data, moves too fast, or does not fit the
structures of our current big data architectures [7]. Big Data is typically large volume of unstructured and
structured data that gets created from various organized and disordered applications, activities and channels such
as emails etc. The main adversity with Big Data includes search, capture, storage, sharing, analysis, and
visualization [2]. The core of Big Data is Hadoop which is a platform for administer computing problems across
a number of servers. It is first developed and released as open source by web application. It implements the Map
Reduce approach pioneered by Google in compiling its search indexes [8]. To store data, Hadoop utilizes its
own distributed file system, HDFS, which makes data available to multiple computing nodes. Big data outbreak
[4], a result not only of increasing web usage by people around the world the connection of billions of devices to
the web.
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III. Hadoop
Hadoop is a platform that provides both shared storage and computational capabilities. At the time Google had
published papers that described its different distributed file system, the Google File System and Map Reduce, a
computational framework for parallel processing. The successful implementation of these paper concepts in
Nutch resulted in its split into more projects, the second of which became Hadoop, in this section will look at
Hadoop architectural perspective, examine how much company’s uses it, and consider some of its weaknesses.
Once we have covered Hadoop background, will look at how to install Hadoop and run a Map Reduce job.
Hadoop is a distributed master and slave architecture that consists of the Hadoop Distributed File System for
storage and Map Reduce [10] for computational capabilities. Traits intrinsic to Hadoop are data partitioning and
parallel computing of large datasets. Its storage and computational capabilities scale with the more number of
hosts to a Hadoop cluster, and can reach volume sizes in the pet bytes on clusters with thousands of hosts. In the
first step in this section will examine the Hadoop Distributed File System and Map Reduce architectures.
Figure 1. Big Data and Hadoop Architecture
IV. Hdfs
The Apache Hadoop is a file system called the Hadoop Distributed File System or simply HDFS. HDFS was
built to support big throughput, processing read and write of extremely large files. Traditional large Network
Attached Storage (NAS) and Storage Area Networks (SANs) offer centralized [1], low-latency access to either a
block device or a file system on the order of terabytes in size. These systems are unusually as the backing store
for large databases, content delivery systems and same types of data storage needs because they can support
full-featured POSIX semantics, scale to meet the size requirements of these systems, and offer lesser-latency
access to data. Imagine for a second [2], though, hundreds or thousands of machines all awakening at the same
time and pulling hundreds of terabytes of data from a central control storage system at once. This is where
conventional storage does not necessarily scale. By creating a system composed of independent machines, each
with its own input output subsystem, network interfaces, hard disks, RAM, and CPU, for the specific type of
workload we are interested in.
Figure 2. HDFS Architecture
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4.1 There are a number of specific goals for HDFS:
Store millions of huge files, each greater than tens of gigabytes, and file system sizes embracing tens of
petabytes.
Use a scale out model based on inexpensive commodity servers with internal JBOD rather than RAID to
achieve large scale storage. Accomplish possibility and high throughput through application level
reproduction of data.
Optimize for large streaming reads and writes rather than low suspension access to many small files. Batch
performance is more important than reciprocal response times.
Gracefully deal with component failures of machines and disks.
Support the performance and scale requirements of Map Reduce processing.
V. Mapreduce
Hadoop Map Reduce is a software framework for freely writing applications which process large amounts of
data in parallel on large clusters of product hardware very trustful, fault-tolerant manner. A Map
Reduce job usually splits the input dataset into self directed collections which are processed by the map tasks in
a completely parallel manner. The framework distinguished the outputs of the maps, which are then input to
the reduce tasks. Typically both the input and the output stored in a file system. The framework takes care of
scheduling tasks, monitoring them and reproduces the failed tasks. Typically the storage nodes and the compute
nodes are the same [3], that is, the Map Reduce framework and the widespread file system are running on the
same set of nodes. This configuration allows the framework to effectively schedule tasks on the nodes where
data is presented before, resulting in very high aggregate bandwidth across the cluster.
The Map Reduce framework consists of a single master [9] Job detector and one slave Taskdetector per cluster-
node. The master is responsible for scheduling the jobs’ element tasks on the slaves, monitoring them and
reproduces the failed tasks. The slaves execute the tasks as assisted by the master. Minimally, applications
specify the input and output locations and provide Map Reduce functions via implementations of perfect
interfaces and or abstract-classes, and other job parameters, composed of the job configuration. The Hadoop job
client then submits the job and configuration to the Jobdetector which then assumes and answerable of
distributing the software configuration to the slaves, scheduling tasks and monitoring them, providing status and
characteristic information to the job client.
Figure 3.MapReduce Architecture in Big Data
5.1 Key Features of Map Reduce Framework
Implement Framework for Map Reduce Execution
Intellectual Developer from the complexity of Distributed programming
Partial failure of the refine cluster is expected and tolerable
Redundancy and fault-tolerance is built in. so that programmer does not have to worry
Map Reduce programming model is language self-sufficient
Self starting parallelization and distribution
Failing tolerance
Enable data local refine
Shared nothing architecture
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VI. Wordcount Example
Wordcount example reads text files and counts how frequently words occur. The input is text files and the
output is word count, each line contains a word and the count of how oftentimes it occurred, divided by a tab.
Each mapper takes a line as input and breaks it into words. It then spit out a key, value pair of the word and 1.
Each reducer sums the counts for each word and spit out a single key, value with the word and sum. As for
improvement of the reducer is also used as a incorporate on the map output. This reduces the amount of data
sent across the network by combining each word into a single record.
Step 1: Running Worcount
$mkdirinputdir
$cd inputdir
$nano input.txt
Figure 4.Create a file
Step 2:Create the jar file and enter some text and save the file, it will be there in local file system only.
Figure 5. Input to a text file
$hadoop jar /home/srikanth/hadoop-2.6.0/share/hadoop/mapreduce/hadoop-mapreduceexamples-2.6.0
wordcount inputdir outputdir to
Figure 6.Create a Jar file
Step 3: wordcount inputdit ouputdir
$ cd outputdir
$ cat part-r-00000
Figure 7. Output for a wordcount
VII. Conclusion
Using big data large volumes of data get created from various applications and activates where these common
technical challenges are very common in various application domains. The data is extremely large where it does
not fit to the structure of current database architectures. The paper describes Local StandaloneMode with Hadoop
which is an open source software used for processing of large volumes of data. The paper directly is based on the
development of HDFS and Hadoop Map Reduce. The Interest and investment in Hadoop has led to an entire
ecosystem of related software both open source and commercial. Within the Apache Software Foundation,
projects are integrated with Hadoop are springing up recurrently. Some of these projects make Map Reduce jobs
easier and more accessible while others focus on getting data in and out of HDFS, simplify operations, and so on.
Based on this paper we can extend the paper with the help of YARN and DFS to execute the word count as a web
based application.
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