Scaling Big Data with Hadoop and Solr Second Edition.Pdf
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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. -
Apache Sentry
Apache Sentry Prasad Mujumdar [email protected] [email protected] Agenda ● Various aspects of data security ● Apache Sentry for authorization ● Key concepts of Apache Sentry ● Sentry features ● Sentry architecture ● Integration with Hadoop ecosystem ● Sentry administration ● Future plans ● Demo ● Questions Who am I • Software engineer at Cloudera • Committer and PPMC member of Apache Sentry • also for Apache Hive and Apache Flume • Part of the the original team that started Sentry work Aspects of security Perimeter Access Visibility Data Authentication Authorization Audit, Lineage Encryption, what user can do data origin, usage Kerberos, LDAP/AD Masking with data Data access Access ● Provide user access to data Authorization ● Manage access policies what user can do ● Provide role based access with data Agenda ● Various aspects of data security ● Apache Sentry for authorization ● Key concepts of Apache Sentry ● Sentry features ● Sentry architecture ● Integration with Hadoop ecosystem ● Sentry administration ● Future plans ● Demo ● Questions Apache Sentry (Incubating) Unified Authorization module for Hadoop Unlocks Key RBAC Requirements Secure, fine-grained, role-based authorization Multi-tenant administration Enforce a common set of policies across multiple data access path in Hadoop. Key Capabilities of Sentry Fine-Grained Authorization Permissions on object hierarchie. Eg, Database, Table, Columns Role-Based Authorization Support for role templetes to manage authorization for a large set of users and data objects Multi Tanent Administration -
Cómo Citar El Artículo Número Completo Más Información Del
DYNA ISSN: 0012-7353 Universidad Nacional de Colombia Iván-Herrera-Herrera, Nelson; Luján-Mora, Sergio; Gómez-Torres, Estevan Ricardo Integración de herramientas para la toma de decisiones en la congestión vehicular DYNA, vol. 85, núm. 205, 2018, Abril-Junio, pp. 363-370 Universidad Nacional de Colombia DOI: https://doi.org/10.15446/dyna.v85n205.67745 Disponible en: https://www.redalyc.org/articulo.oa?id=49657889045 Cómo citar el artículo Número completo Sistema de Información Científica Redalyc Más información del artículo Red de Revistas Científicas de América Latina y el Caribe, España y Portugal Página de la revista en redalyc.org Proyecto académico sin fines de lucro, desarrollado bajo la iniciativa de acceso abierto Integration of tools for decision making in vehicular congestion• Nelson Iván-Herrera-Herreraa, Sergio Luján-Morab & Estevan Ricardo Gómez-Torres a a Facultad de Ciencias de la Ingeniería e Industrias, Universidad Tecnológica Equinoccial, Quito, Ecuador. [email protected], [email protected] b Departamento de Lenguajes y Sistemas Informáticos, Universidad de Alicante, Alicante, España. [email protected] Received: September 15th, 2017. Received in revised form: March 15th, 2018. Accepted: March 21th, 2018. Abstract The purpose of this study is to present an analysis of the use and integration of technological tools that help decision making in situations of vehicular congestion. The city of Quito-Ecuador is considered as a case study for the done work. The research is presented according to the development of an application, using Big Data tools (Apache Flume, Apache Hadoop, Apache Pig), favoring the processing of a lot of information that is required to collect, store and process. -
Enterprise Search Technology Using Solr and Cloud Padmavathy Ravikumar Governors State University
Governors State University OPUS Open Portal to University Scholarship All Capstone Projects Student Capstone Projects Spring 2015 Enterprise Search Technology Using Solr and Cloud Padmavathy Ravikumar Governors State University Follow this and additional works at: http://opus.govst.edu/capstones Part of the Databases and Information Systems Commons Recommended Citation Ravikumar, Padmavathy, "Enterprise Search Technology Using Solr and Cloud" (2015). All Capstone Projects. 91. http://opus.govst.edu/capstones/91 For more information about the academic degree, extended learning, and certificate programs of Governors State University, go to http://www.govst.edu/Academics/Degree_Programs_and_Certifications/ Visit the Governors State Computer Science Department This Project Summary is brought to you for free and open access by the Student Capstone Projects at OPUS Open Portal to University Scholarship. It has been accepted for inclusion in All Capstone Projects by an authorized administrator of OPUS Open Portal to University Scholarship. For more information, please contact [email protected]. ENTERPRISE SEARCH TECHNOLOGY USING SOLR AND CLOUD By Padmavathy Ravikumar Masters Project Submitted in partial fulfillment of the requirements For the Degree of Master of Science, With a Major in Computer Science Governors State University University Park, IL 60484 Fall 2014 ENTERPRISE SEARCH TECHNOLOGY USING SOLR AND CLOUD 2 Abstract Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database in9tegration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. -
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 -
Database Licensing Information User Manual
Oracle® Database Database Licensing Information User Manual 19c E94254-04 April 2019 Oracle Database Database Licensing Information User Manual, 19c E94254-04 Copyright © 2004, 2019, Oracle and/or its affiliates. All rights reserved. Contributors: Penny Avril, Prabhaker Gongloor, Mughees Minhas, Anu Natarajan, Jill Robinson This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited. The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this is software or related documentation that is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, then the following notice is applicable: U.S. GOVERNMENT END USERS: Oracle programs, including any operating system, integrated software, any programs installed on the hardware, and/or documentation, delivered to U.S. Government end users are "commercial computer software" pursuant to the applicable Federal Acquisition Regulation and agency- specific supplemental regulations. As such, use, duplication, disclosure, modification, and adaptation of the programs, including any operating system, integrated software, any programs installed on the hardware, and/or documentation, shall be subject to license terms and license restrictions applicable to the programs. -
Final Report CS 5604: Information Storage and Retrieval
Final Report CS 5604: Information Storage and Retrieval Solr Team Abhinav Kumar, Anand Bangad, Jeff Robertson, Mohit Garg, Shreyas Ramesh, Siyu Mi, Xinyue Wang, Yu Wang January 16, 2018 Instructed by Professor Edward A. Fox Virginia Polytechnic Institute and State University Blacksburg, VA 24061 1 Abstract The Digital Library Research Laboratory (DLRL) has collected over 1.5 billion tweets and millions of webpages for the Integrated Digital Event Archiving and Library (IDEAL) and Global Event Trend Archive Research (GETAR) projects [6]. We are using a 21 node Cloudera Hadoop cluster to store and retrieve this information. One goal of this project is to expand the data collection to include more web archives and geospatial data beyond what previously had been collected. Another important part in this project is optimizing the current system to analyze and allow access to the new data. To accomplish these goals, this project is separated into 6 parts with corresponding teams: Classification (CLA), Collection Management Tweets (CMT), Collection Management Webpages (CMW), Clustering and Topic Analysis (CTA), Front-end (FE), and SOLR. This report describes the work completed by the SOLR team which improves the current searching and storage system. We include the general architecture and an overview of the current system. We present the part that Solr plays within the whole system with more detail. We talk about our goals, procedures, and conclusions on the improvements we made to the current Solr system. This report also describes how we coordinate with other teams to accomplish the project at a higher level. Additionally, we provide manuals for future readers who might need to replicate our experiments. -
An Unobtrusive, Scalable Approach to Large Scale Software License Analysis
DRAT: An Unobtrusive, Scalable Approach to Large Scale Software License Analysis Chris A. Mattmann1,2, Ji-Hyun Oh1,2, Tyler Palsulich1*, Lewis John McGibbney1, Yolanda Gil2,3, Varun Ratnakar3 1Jet Propulsion Laboratory 2Computer Science Department 3USC Information Sciences Institute California Institute of Technology University of Southern California University of Southern California Pasadena, CA 91109 USA Los Angeles, CA 90089 USA Marina Del Rey, CA [email protected] {mattmann,jihyuno}@usc.edu {gil,varunr}@ isi.edu Abstract— The Apache Release Audit Tool (RAT) performs (OSI) for complying with open source software open source license auditing and checking, however definition, however, there exist slight differences among these RAT fails to successfully audit today's large code bases. Being a licenses [2]. For instance, GPL is a “copyleft” license that natural language processing (NLP) tool and a crawler, RAT only allows derivative works under the original license, marches through a code base, but uses rudimentary black lists whereas MIT license is a “permissive” license that grants the and white lists to navigate source code repositories, and often does a poor job of identifying source code versus binary files. In right to sublicense the code under any kind of license [2]. This addition RAT produces no incremental output and thus on code difference could affect architectural design of the software. bases that themselves are "Big Data", RAT could run for e.g., a Furthermore, circumstances are more complicated when month and still not provide any status report. We introduce people publish software under the multiple licenses. Distributed "RAT" or the Distributed Release Audit Tool Therefore, an automated tool for verifying software licenses in (DRAT). -
Extended Version
Sina Sheikholeslami C u rriculum V it a e ( Last U pdated N ovember 2 0 18) Website: http://sinash.ir Present Address : https://www.kth.se/profile/sinash EIT Digital Stockholm CLC , https://linkedin.com/in/sinasheikholeslami Isafjordsgatan 26, Email: si [email protected] 164 40 Kista (Stockholm), [email protected] Sweden [email protected] Educational Background: • M.Sc. Student of Data Science, Eindhoven University of Technology & KTH Royal Institute of Technology, Under EIT-Digital Master School. 2017-Present. • B.Sc. in Computer Software Engineering, Department of Computer Engineering and Information Technology, Amirkabir University of Technology (Tehran Polytechnic). 2011-2016. • Mirza Koochak Khan Pre-College in Mathematics and Physics, Rasht, National Organization for Development of Exceptional Talents (NODET). Overall GPA: 19.61/20. 2010-2011. • Mirza Koochak Khan Highschool in Mathematics and Physics, Rasht, National Organization for Development of Exceptional Talents (NODET). Overall GPA: 19.17/20, Final Year's GPA: 19.66/20. 2007-2010. Research Fields of Interest: • Distributed Deep Learning, Hyperparameter Optimization, AutoML, Data Intensive Computing Bachelor's Thesis: • “SDMiner: A Tool for Mining Data Streams on Top of Apache Spark”, Under supervision of Dr. Amir H. Payberah (Defended on June 29th 2016). Computer Skills: • Programming Languages & Markups: o F luent in Java, Python, Scala, JavaS cript, C/C++, A ndroid Pr ogram Develop ment o Familia r wit h R, SAS, SQL , Nod e.js, An gula rJS, HTM L, JSP • -
Vuln4real: a Methodology for Counting Actually Vulnerable Dependencies
This is the author's version of an article that has been published in this journal. Changes were made to this version by the publisher prior to publication. The final version of record is available at http://dx.doi.org/10.1109/TSE.2020.3025443 1 Vuln4Real: A Methodology for Counting Actually Vulnerable Dependencies Ivan Pashchenko, Henrik Plate, Serena Elisa Ponta, Antonino Sabetta, and Fabio Massacci Abstract— Vulnerable dependencies are a known problem in today’s free open-source software ecosystems because FOSS libraries are highly interconnected, and developers do not always update their dependencies. Our paper proposes Vuln4Real, the methodology for counting actually vulnerable dependencies, that addresses the over-inflation problem of academic and industrial approaches for reporting vulnerable dependencies in FOSS software, and therefore, caters to the needs of industrial practice for correct allocation of development and audit resources. To understand the industrial impact of a more precise methodology, we considered the 500 most popular FOSS Java libraries used by SAP in its own software. Our analysis included 25767 distinct library instances in Maven. We found that the proposed methodology has visible impacts on both ecosystem view and the individual library developer view of the situation of software dependencies: Vuln4Real significantly reduces the number of false alerts for deployed code (dependencies wrongly flagged as vulnerable), provides meaningful insights on the exposure to third-parties (and hence vulnerabilities) of a library, and automatically predicts when dependency maintenance starts lagging, so it may not receive updates for arising issues. Index Terms—Vulnerable Dependency; Free Open Source Software; Mining Software Repositories F 1 INTRODUCTION (they may belong to the same project), and therefore, should be treated as a single unit, when constructing dependency The inclusion of free open-source software (FOSS) com- trees and reporting results of a dependency study.