Transformation of Data from RDBMS to HDFS by Using Load Atomizer
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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. -
E6895 Advanced Big Data Analytics Lecture 4: Data Store
E6895 Advanced Big Data Analytics Lecture 4: Data Store Ching-Yung Lin, Ph.D. Adjunct Professor, Dept. of Electrical Engineering and Computer Science Chief Scientist, Graph Computing, IBM Watson Research Center E6895 Advanced Big Data Analytics — Lecture 4 © CY Lin, 2016 Columbia University Reference 2 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Spark SQL 3 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Spark SQL 4 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Apache Hive 5 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Using Hive to Create a Table 6 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Creating, Dropping, and Altering DBs in Apache Hive 7 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Another Hive Example 8 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Hive’s operation modes 9 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Using HiveQL for Spark SQL 10 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Hive Language Manual 11 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Using Spark SQL — Steps and Example 12 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Query testtweet.json Get it from Learning Spark Github ==> https://github.com/databricks/learning-spark/tree/master/files 13 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University SchemaRDD 14 E6895 Advanced Big Data Analytics – Lecture 4: Data Store © 2015 CY Lin, Columbia University Row Objects Row objects represent records inside SchemaRDDs, and are simply fixed-length arrays of fields. -
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. -
Mobile Server Deployment and Configuration Guide Content
CUSTOMER SAP BusinessObjects Mobile Document Version: 4.2 SP6 – 2017-12-15 Mobile Server Deployment and Configuration Guide Content 1 Document History..............................................................5 2 Target Audience............................................................... 6 3 Introducing the SAP BusinessObjects Mobile Solution.................................. 7 3.1 Solution Overview...............................................................7 SAP BusinessObjects Mobile Client................................................8 SAP BusinessObjects Mobile Server................................................8 SAP BusinessObjects Business Intelligence (BI) Platform.................................9 4 Deploying the SAP BusinessObjects Mobile Server Package.............................10 4.1 Pre-Installation Checklist..........................................................11 4.2 Deploying Server Package using WDeploy..............................................12 4.3 Configuring Your Web application Server.............................................. 13 SAP NetWeaver Web Application Server ............................................13 WebSphere Application Server ...................................................14 WebLogic Web Application Server.................................................14 JBoss Web Application Server................................................... 15 4.4 Auto-Deployment of Mobile Server.................................................. 15 4.5 Deploying SAP Lumira Server on Unsupported -
California State University, Northridge the Design And
CALIFORNIA STATE UNIVERSITY, NORTHRIDGE THE DESIGN AND IMPLEMENTATION OF A SMALL TO MEDIUM RESTAURANT BUSINESS WEB APPLICATION A graduate project submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science By Edward Gerhardstein May 2011 The graduate project of Edward Gerhardstein is approved: John Noga , Ph.D. Date Robert McIlhenny , Ph.D. Date Jeff Wiegley , Ph.D., Chair Date California State University, Northridge ii Table of Contents Signature page ii Abstract vi 1 Overview of Pizza Application 1 2 Open Source Licenses Servers 2 2.1 Open Source License Definition . .2 2.2 Ubuntu . .2 2.3 Apache Tomcat . .2 2.4 MySQL . .4 3 Selected Concepts and Terminologies 6 3.1 Model-View-Controller (MVC) . .6 3.2 JavaScript . .7 3.3 Ajax . .7 3.4 XML . .7 3.5 DTD . .7 3.6 XML Schema . .7 3.7 CSS . .8 4 J2EE Concepts 9 4.1 J2EE Overview . .9 4.2 JavaBean . .9 4.3 Enterprise JavaBeans (EJB) . .9 4.4 Other J2EE APIs and Technologies . .9 4.5 Servlets . 10 4.6 JavaServer Pages (JSP) . 11 4.6.1 Scriptlet . 11 5 Apache Struts Framework 13 5.1 Apache Struts Overview . 13 5.2 ActionServlet . 13 5.3 Struts Config . 13 6 Pizza Application Overview 15 6.1 Design Layout . 15 6.2 Workflow . 15 6.3 JSP Page formats - Index.jsp/Templates . 17 6.4 JSP Page Divisions . 18 7 ClockIn/Clockout and Logon Functionality 21 7.1 ClockIn/Clockout Functionality . 21 iii 7.2 Logon Functionality . 21 8 Administrator Functionality 24 8.1 Administrator Functionality Description . -
Unravel Data Systems Version 4.5
UNRAVEL DATA SYSTEMS VERSION 4.5 Component name Component version name License names jQuery 1.8.2 MIT License Apache Tomcat 5.5.23 Apache License 2.0 Tachyon Project POM 0.8.2 Apache License 2.0 Apache Directory LDAP API Model 1.0.0-M20 Apache License 2.0 apache/incubator-heron 0.16.5.1 Apache License 2.0 Maven Plugin API 3.0.4 Apache License 2.0 ApacheDS Authentication Interceptor 2.0.0-M15 Apache License 2.0 Apache Directory LDAP API Extras ACI 1.0.0-M20 Apache License 2.0 Apache HttpComponents Core 4.3.3 Apache License 2.0 Spark Project Tags 2.0.0-preview Apache License 2.0 Curator Testing 3.3.0 Apache License 2.0 Apache HttpComponents Core 4.4.5 Apache License 2.0 Apache Commons Daemon 1.0.15 Apache License 2.0 classworlds 2.4 Apache License 2.0 abego TreeLayout Core 1.0.1 BSD 3-clause "New" or "Revised" License jackson-core 2.8.6 Apache License 2.0 Lucene Join 6.6.1 Apache License 2.0 Apache Commons CLI 1.3-cloudera-pre-r1439998 Apache License 2.0 hive-apache 0.5 Apache License 2.0 scala-parser-combinators 1.0.4 BSD 3-clause "New" or "Revised" License com.springsource.javax.xml.bind 2.1.7 Common Development and Distribution License 1.0 SnakeYAML 1.15 Apache License 2.0 JUnit 4.12 Common Public License 1.0 ApacheDS Protocol Kerberos 2.0.0-M12 Apache License 2.0 Apache Groovy 2.4.6 Apache License 2.0 JGraphT - Core 1.2.0 (GNU Lesser General Public License v2.1 or later AND Eclipse Public License 1.0) chill-java 0.5.0 Apache License 2.0 Apache Commons Logging 1.2 Apache License 2.0 OpenCensus 0.12.3 Apache License 2.0 ApacheDS Protocol -
Talend Open Studio for Big Data Release Notes
Talend Open Studio for Big Data Release Notes 6.0.0 Talend Open Studio for Big Data Adapted for v6.0.0. Supersedes previous releases. Publication date July 2, 2015 Copyleft This documentation is provided under the terms of the Creative Commons Public License (CCPL). For more information about what you can and cannot do with this documentation in accordance with the CCPL, please read: http://creativecommons.org/licenses/by-nc-sa/2.0/ Notices Talend is a trademark of Talend, Inc. All brands, product names, company names, trademarks and service marks are the properties of their respective owners. License Agreement The software described in this documentation is licensed under the Apache License, Version 2.0 (the "License"); you may not use this software except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0.html. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. This product includes software developed at AOP Alliance (Java/J2EE AOP standards), ASM, Amazon, AntlR, Apache ActiveMQ, Apache Ant, Apache Avro, Apache Axiom, Apache Axis, Apache Axis 2, Apache Batik, Apache CXF, Apache Cassandra, Apache Chemistry, Apache Common Http Client, Apache Common Http Core, Apache Commons, Apache Commons Bcel, Apache Commons JxPath, Apache -
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 -
Hive, Spark, Presto for Interactive Queries on Big Data
DEGREE PROJECT IN INFORMATION AND COMMUNICATION TECHNOLOGY, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2018 Hive, Spark, Presto for Interactive Queries on Big Data NIKITA GUREEV KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE TRITA TRITA-EECS-EX-2018:468 www.kth.se Abstract Traditional relational database systems can not be efficiently used to analyze data with large volume and different formats, i.e. big data. Apache Hadoop is one of the first open-source tools that provides a dis- tributed data storage system and resource manager. The space of big data processing has been growing fast over the past years and many tech- nologies have been introduced in the big data ecosystem to address the problem of processing large volumes of data, and some of the early tools have become widely adopted, with Apache Hive being one of them. How- ever, with the recent advances in technology, there are other tools better suited for interactive analytics of big data, such as Apache Spark and Presto. In this thesis these technologies are examined and benchmarked in or- der to determine their performance for the task of interactive business in- telligence queries. The benchmark is representative of interactive business intelligence queries, and uses a star-shaped schema. The performance Hive Tez, Hive LLAP, Spark SQL, and Presto is examined with text, ORC, Par- quet data on different volume and concurrency. A short analysis and con- clusions are presented with the reasoning about the choice of framework and data format for a system that would run interactive queries on big data.