A User-Centered and Autonomic Multi-Cloud Architecture for High Performance Computing Applications
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Hadoop Tutorials Cassandra Hector API Request Tutorial About
Home Big Data Hadoop Tutorials Cassandra Hector API Request Tutorial About LABELS: HADOOP-TUTORIAL, HDFS 3 OCTOBER 2013 Hadoop Tutorial: Part 1 - What is Hadoop ? (an Overview) Hadoop is an open source software framework that supports data intensive distributed applications which is licensed under Apache v2 license. At-least this is what you are going to find as the first line of definition on Hadoop in Wikipedia. So what is data intensive distributed applications? Well data intensive is nothing but BigData (data that has outgrown in size) anddistributed applications are the applications that works on network by communicating and coordinating with each other by passing messages. (say using a RPC interprocess communication or through Message-Queue) Hence Hadoop works on a distributed environment and is build to store, handle and process large amount of data set (in petabytes, exabyte and more). Now here since i am saying that hadoop stores petabytes of data, this doesn't mean that Hadoop is a database. Again remember its a framework that handles large amount of data for processing. You will get to know the difference between Hadoop and Databases (or NoSQL Databases, well that's what we call BigData's databases) as you go down the line in the coming tutorials. Hadoop was derived from the research paper published by Google on Google File System(GFS) and Google's MapReduce. So there are two integral parts of Hadoop: Hadoop Distributed File System(HDFS) and Hadoop MapReduce. Hadoop Distributed File System (HDFS) HDFS is a filesystem designed for storing very large files with streaming data accesspatterns, running on clusters of commodity hardware. -
MÁSTER EN INGENIERÍA WEB Proyecto Fin De Máster
UNIVERSIDAD POLITÉCNICA DE MADRID Escuela Técnica Superior de Ingeniería de Sistemas Informáticos MÁSTER EN INGENIERÍA WEB Proyecto Fin de Máster …Estudio Conceptual de Big Data utilizando Spring… Autor Gabriel David Muñumel Mesa Tutor Jesús Bernal Bermúdez 1 de julio de 2018 Estudio Conceptual de Big Data utilizando Spring AGRADECIMIENTOS Gracias a mis padres Julian y Miriam por todo el apoyo y empeño en que siempre me mantenga estudiando. Gracias a mi tia Gloria por sus consejos e ideas. Gracias a mi hermano José Daniel y mi cuñada Yule por siempre recordarme que con trabajo y dedicación se pueden alcanzar las metas. [UPM] Máster en Ingeniería Web RESUMEN Big Data ha sido el término dado para aglomerar la gran cantidad de datos que no pueden ser procesados por los métodos tradicionales. Entre sus funciones principales se encuentran la captura de datos, almacenamiento, análisis, búsqueda, transferencia, visualización, monitoreo y modificación. Las empresas han visto en Big Data una poderosa herramienta para mejorar sus negocios en una economía mundial basada firmemente en el conocimiento. Los datos son el combustible para las compañías modernas y, por lo tanto, dar sentido a estos datos permite realmente comprender las conexiones invisibles dentro de su origen. En efecto, con mayor información se toman mejores decisiones, permitiendo la creación de estrategias integrales e innovadoras que garanticen resultados exitosos. Dada la creciente relevancia de Big Data en el entorno profesional moderno ha servido como motivación para la realización de este proyecto. Con la utilización de Java como software de desarrollo y Spring como framework web se desea analizar y comprobar qué herramientas ofrecen estas tecnologías para aplicar procesos enfocados en Big Data. -
Security Log Analysis Using Hadoop Harikrishna Annangi Harikrishna Annangi, [email protected]
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by St. Cloud State University St. Cloud State University theRepository at St. Cloud State Culminating Projects in Information Assurance Department of Information Systems 3-2017 Security Log Analysis Using Hadoop Harikrishna Annangi Harikrishna Annangi, [email protected] Follow this and additional works at: https://repository.stcloudstate.edu/msia_etds Recommended Citation Annangi, Harikrishna, "Security Log Analysis Using Hadoop" (2017). Culminating Projects in Information Assurance. 19. https://repository.stcloudstate.edu/msia_etds/19 This Starred Paper is brought to you for free and open access by the Department of Information Systems at theRepository at St. Cloud State. It has been accepted for inclusion in Culminating Projects in Information Assurance by an authorized administrator of theRepository at St. Cloud State. For more information, please contact [email protected]. Security Log Analysis Using Hadoop by Harikrishna Annangi A Starred Paper Submitted to the Graduate Faculty of St. Cloud State University in Partial Fulfillment of the Requirements for the Degree of Master of Science in Information Assurance April, 2016 Starred Paper Committee: Dr. Dennis Guster, Chairperson Dr. Susantha Herath Dr. Sneh Kalia 2 Abstract Hadoop is used as a general-purpose storage and analysis platform for big data by industries. Commercial Hadoop support is available from large enterprises, like EMC, IBM, Microsoft and Oracle and Hadoop companies like Cloudera, Hortonworks, and Map Reduce. Hadoop is a scheme written in Java that allows distributed processes of large data sets across clusters of computers using programming models. A Hadoop frame work application works in an environment that provides storage and computation across clusters of computers. -
Orchestrating Big Data Analysis Workflows in the Cloud: Research Challenges, Survey, and Future Directions
00 Orchestrating Big Data Analysis Workflows in the Cloud: Research Challenges, Survey, and Future Directions MUTAZ BARIKA, University of Tasmania SAURABH GARG, University of Tasmania ALBERT Y. ZOMAYA, University of Sydney LIZHE WANG, China University of Geoscience (Wuhan) AAD VAN MOORSEL, Newcastle University RAJIV RANJAN, Chinese University of Geoscienes and Newcastle University Interest in processing big data has increased rapidly to gain insights that can transform businesses, government policies and research outcomes. This has led to advancement in communication, programming and processing technologies, including Cloud computing services and technologies such as Hadoop, Spark and Storm. This trend also affects the needs of analytical applications, which are no longer monolithic but composed of several individual analytical steps running in the form of a workflow. These Big Data Workflows are vastly different in nature from traditional workflows. Researchers arecurrently facing the challenge of how to orchestrate and manage the execution of such workflows. In this paper, we discuss in detail orchestration requirements of these workflows as well as the challenges in achieving these requirements. We alsosurvey current trends and research that supports orchestration of big data workflows and identify open research challenges to guide future developments in this area. CCS Concepts: • General and reference → Surveys and overviews; • Information systems → Data analytics; • Computer systems organization → Cloud computing; Additional Key Words and Phrases: Big Data, Cloud Computing, Workflow Orchestration, Requirements, Approaches ACM Reference format: Mutaz Barika, Saurabh Garg, Albert Y. Zomaya, Lizhe Wang, Aad van Moorsel, and Rajiv Ranjan. 2018. Orchestrating Big Data Analysis Workflows in the Cloud: Research Challenges, Survey, and Future Directions. -
Apache Oozie the Workflow Scheduler for Hadoop
Apache Oozie The Workflow Scheduler For Hadoop televises:Hookier and he sopraninopip his rationalists Jere decrescendo usuriously hisand footie fragilely. inundates Larry disburdenchummed educationally.untimely. Seismographical Evan Apache Zookepeer Tutorial Zookeeper in Hadoop Hadoop. Oozie offers replacement only be used files into hadoop ecosystem components are used, including but for any. The below and action, time if we saw how does flipkart first emi option. Here, how to reduce their costs and increase the time to market. Are whole a Author? Who uses Apache Oozie? What is the estimated delivery time? Oozie operates by running with a prior in a Hadoop cluster with clients submitting workflow definitions for sink or delayed processing. Specifies that cannot span file. Explanation Oozie is a workflow scheduler system where manage Hadoop jobs. Other events and schedule apache storm for all set of a free. Oozie server using REST. Supermart is available only in select cities. Action contains description of hangover or more workflows to be executed Oozie is lightweight as it uses existing Hadoop MapReduce framework for. For sellers on a great features: they implemented has been completed. For example, TORT OR hassle, and SSH. Apache Oozie provides you the power to easily handle these kinds of scenarios. Have doubts regarding this product? Oozie is a workflow scheduler system better manage apache hadoop jobs Oozie workflow jobs are directed acyclical graphs dags of actions By. Recipient as is required. Needed when any oozie client is anger on separated node. Sorry, French, Straus and Giroux. Data pipeline job scheduling in GoDaddy Developer's point of. -
Persisting Big-Data the Nosql Landscape
Information Systems 63 (2017) 1–23 Contents lists available at ScienceDirect Information Systems journal homepage: www.elsevier.com/locate/infosys Persisting big-data: The NoSQL landscape Alejandro Corbellini n, Cristian Mateos, Alejandro Zunino, Daniela Godoy, Silvia Schiaffino ISISTAN (CONICET-UNCPBA) Research Institute1, UNICEN University, Campus Universitario, Tandil B7001BBO, Argentina article info abstract Article history: The growing popularity of massively accessed Web applications that store and analyze Received 11 March 2014 large amounts of data, being Facebook, Twitter and Google Search some prominent Accepted 21 July 2016 examples of such applications, have posed new requirements that greatly challenge tra- Recommended by: G. Vossen ditional RDBMS. In response to this reality, a new way of creating and manipulating data Available online 30 July 2016 stores, known as NoSQL databases, has arisen. This paper reviews implementations of Keywords: NoSQL databases in order to provide an understanding of current tools and their uses. NoSQL databases First, NoSQL databases are compared with traditional RDBMS and important concepts are Relational databases explained. Only databases allowing to persist data and distribute them along different Distributed systems computing nodes are within the scope of this review. Moreover, NoSQL databases are Database persistence divided into different types: Key-Value, Wide-Column, Document-oriented and Graph- Database distribution Big data oriented. In each case, a comparison of available databases -
Native Cloud Applications: Why Virtual Machines, Images and Containers Miss the Point!
Institute of Architecture of Application Systems Native Cloud Applications: Why Virtual Machines, Images and Containers Miss the Point! Frank Leymann, Christoph Fehling, Sebastian Wagner, Johannes Wettinger Institute of Architecture of Application Systems, University of Stuttgart, Germany, [email protected] : @inproceedings{INPROC-2016-19, author = {Frank Leymann, Christoph Fehling, Sebastian Wagner and Johannes Wettinger}, title = {Native Cloud Applications: Why Virtual Machines, Images and Containers Miss the Point!}, booktitle = {Proceedings of the 6th International Conference on Cloud Computing and Service Science (CLOSER 2016)}, year = {2016}, pages = {7-- 15}, publisher = {SciTePress} } These publication and contributions have been presented at CLOSER 2016 http://closer.scitevents.org © 2016 SciTePress. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the SciTePress. Native Cloud Applications: Why Virtual Machines, Images and Containers Miss the Point! Frank Leymann, Christoph Fehling, Sebastian Wagner and Johannes Wettinger Institute of Architecture of Application Systems, Universität Stuttgart, Universitätsstraße 38, Stuttgart, Germany {leymann, fehling, wagner, wettinger}@iaas.uni-stuttgart.de Keywords: Cloud Computing, Virtualization, Cloud Migration, SOA, Microservices, Continuous Delivery Abstract: Due to the current hype around cloud computing, the term “native cloud application” becomes increasingly popular. It suggests an application to fully benefit from all the advantages of cloud computing. Many users tend to consider their applications as cloud native if the application is just bundled in a virtual machine image or a container. -
Analysis of Web Log Data Using Apache Pig in Hadoop
[VOLUME 5 I ISSUE 2 I APRIL – JUNE 2018] e ISSN 2348 –1269, Print ISSN 2349-5138 http://ijrar.com/ Cosmos Impact Factor 4.236 ANALYSIS OF WEB LOG DATA USING APACHE PIG IN HADOOP A. C. Priya Ranjani* & Dr. M. Sridhar** *Research Scholar, Department of Computer Science, Acharya Nagarjuna University, Guntur, Andhra Pradesh, INDIA, **Associate Professor, Department of Computer Applications, R.V.R & J.C College of Engineering, Guntur, India Received: April 09, 2018 Accepted: May 22, 2018 ABSTRACT The wide spread use of internet and increased web applications accelerate the rampant growth of web content. Every organization produces huge amount of data in different forms like text, audio, video etc., from multiplesources. The log data stored in web servers is a great source of knowledge. The real challenge for any organization is to understand the behavior of their customers. Analyzing such web log data will help the organizations to understand navigational patterns and interests of their users. As the logs are growing in size day by day, the existing database technologies face a bottleneck to process such massive unstructured data. Hadoop provides a best solution to this problem. Hadoop framework comes up with Hadoop Distributed File System, a reliable distributed storage for data and MapReduce, a distributed parallel processing for executing large volumes of complex data. Hadoop ecosystem constitutes of several other tools like Pig, Hive, Flume, Sqoop etc., for effective analysis of web log data. To write scripts in Map Reduce, one should acquire a good programming knowledge in Java. However Pig, a simple dataflow language can be easily used to analyze such data. -
Apache Pig's Optimizer
Apache Pig’s Optimizer Alan F. Gates, Jianyong Dai, Thejas Nair Hortonworks Abstract Apache Pig allows users to describe dataflows to be executed in Apache Hadoop. The distributed nature of Hadoop, as well as its execution paradigms, provide many execution opportunities as well as impose constraints on the system. Given these opportunities and constraints Pig must make decisions about how to optimize the execution of user scripts. This paper covers some of those optimization choices, focussing one ones that are specific to the Hadoop ecosystem and Pig’s common use cases. It also discusses optimizations that the Pig community has considered adding in the future. 1 Introduction Apache Pig [10] provides an SQL-like dataflow language on top of Apache Hadoop [11] [7]. With Pig, users write dataflow scripts in a language called Pig Latin. Pig then executes these dataflow scripts in Hadoop using MapReduce. Providing users with a scripting language, rather than requiring them to write MapReduce pro- grams in Java, drastically decreases their development time and enables non-Java developers to use Hadoop. Pig also provides operators for most common data processing operations, such as join, sort, and aggregation. It would otherwise require huge amounts of effort for a handcrafted Java MapReduce program to implement these operators. Many different types of data processing are done on Hadoop. Pig does not seek to be a general purpose solution for all of them. Pig focusses on use cases where users have a DAG of transformations to be done on their data, involving some combination of standard relational operations (join, aggregation, etc.) and custom processing which can be included in Pig Latin via User Defined Functions, or UDFs, which can be written in Java or a scripting language.1 Pig also focusses on situations where data may not yet be cleansed and normal- ized. -
Serverless Cloud Computing: a Comparison Between "Function As a Service"
SERVERLESS CLOUD COMPUTING : A COMPARISON BETWEEN "F UNCTION AS A SERVICE " P LATFORMS Víctor Juan Expósito Jiménez and Herwig Zeiner JOANNEUM RESEARCH Forschungsgesellschaft mbH, Steyrergasse, Graz, Austria ABSTRACT Cloud computing is moving fast and continually progressing. Beyond the microservices architecture, a new paradigm appears to be evolving and complementing it. By using a serverless computing architecture, faster and more reliable developments are possible in several fields such as the Internet of Things, industrial or mobility applications. In this paper, a serverless computing architecture is described and, in addition, a comparison of the most important "Function as a Service" platforms is given. KEYWORDS Serverless Architecture, Function as a Service (FaaS), Cloud Computing, Microservices 1. I NTRODUCTION The Internet of Things (IoT) has changed the way applications are designed. Billions of devices will be connected to the network in a near future. The next generation of connected applications has to change the way they have been designed to support this exponential growth of connected devices and their corresponding services. At the moment, a microservices architecture can fulfill these requirements where the combination of different services is a main issue. In this kind of cloud computing architecture, all tasks and the logic are split in small services, where each is able to serve just one specialized purpose instead of one monolithic component for all purposes. This allows a more versatile development in which maintenance and updates can be done only to the needed components without changing the whole core system. Every service is isolated and all information and all controllers are accessible through external APIs. -
Polycom UC Software 5.4.3 Administrator Guide
ADMINISTRATOR GUIDE UC Software 5.4.3 | March 2016 | 3725-49104-010A Polycom® UC Software 5.4.3 Copyright© 2016, Polycom, Inc. All rights reserved. No part of this document may be reproduced, translated into another language or format, or transmitted in any form or by any means, electronic or mechanical, for any purpose, without the express written permission of Polycom, Inc. 6001 America Center Drive San Jose, CA 95002 USA Trademarks Polycom®, the Polycom logo and the names and marks associated with Polycom products are trademarks and/or service marks of Polycom, Inc. and are registered and/or common law marks in the United States and various other countries. All other trademarks are property of their respective owners. No portion hereof may be reproduced or transmitted in any form or by any means, for any purpose other than the recipient's personal use, without the express written permission of Polycom. Disclaimer While Polycom uses reasonable efforts to include accurate and up-to-date information in this document, Polycom makes no warranties or representations as to its accuracy. Polycom assumes no liability or responsibility for any typographical or other errors or omissions in the content of this document. Limitation of Liability Polycom and/or its respective suppliers make no representations about the suitability of the information contained in this document for any purpose. Information is provided "as is" without warranty of any kind and is subject to change without notice. The entire risk arising out of its use remains with the recipient. In no event shall Polycom and/or its respective suppliers be liable for any direct, consequential, incidental, special, punitive or other damages whatsoever (including without limitation, damages for loss of business profits, business interruption, or loss of business information), even if Polycom has been advised of the possibility of such damages. -
HDP 3.1.4 Release Notes Date of Publish: 2019-08-26
Release Notes 3 HDP 3.1.4 Release Notes Date of Publish: 2019-08-26 https://docs.hortonworks.com Release Notes | Contents | ii Contents HDP 3.1.4 Release Notes..........................................................................................4 Component Versions.................................................................................................4 Descriptions of New Features..................................................................................5 Deprecation Notices.................................................................................................. 6 Terminology.......................................................................................................................................................... 6 Removed Components and Product Capabilities.................................................................................................6 Testing Unsupported Features................................................................................ 6 Descriptions of the Latest Technical Preview Features.......................................................................................7 Upgrading to HDP 3.1.4...........................................................................................7 Behavioral Changes.................................................................................................. 7 Apache Patch Information.....................................................................................11 Accumulo...........................................................................................................................................................