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Netapp Solutions for Hadoop Reference Architecture: Cloudera Faiz Abidi (Netapp) and Udai Potluri (Cloudera) June 2018 | WP-7217
White Paper NetApp Solutions for Hadoop Reference Architecture: Cloudera Faiz Abidi (NetApp) and Udai Potluri (Cloudera) June 2018 | WP-7217 In partnership with Abstract There has been an exponential growth in data over the past decade and analyzing huge amounts of data in a reasonable time can be a challenge. Apache Hadoop is an open- source tool that can help your organization quickly mine big data and extract meaningful patterns from it. However, enterprises face several technical challenges when deploying Hadoop, specifically in the areas of cluster availability, operations, and scaling. NetApp® has developed a reference architecture with Cloudera to deliver a solution that overcomes some of these challenges so that businesses can ingest, store, and manage big data with greater reliability and scalability and with less time spent on operations and maintenance. This white paper discusses a flexible, validated, enterprise-class Hadoop architecture that is based on NetApp E-Series storage using Cloudera’s Hadoop distribution. TABLE OF CONTENTS 1 Introduction ........................................................................................................................................... 4 1.1 Big Data ..........................................................................................................................................................4 1.2 Hadoop Overview ...........................................................................................................................................4 2 NetApp E-Series -
Commonjavajars - a Package with Useful Libraries for Java Guis
CommonJavaJars - A package with useful libraries for Java GUIs To reduce the package size of other R packages with Java GUIs and to reduce jar file conflicts, this package provides a few commonly used Java libraries. You should be able to load them by calling the rJava .jpackage function (a good place is most likely the .onLoad function of your package): .jpackage("CommonJavaJars", jars=c("forms-1.2.0.jar", "iText-2.1.4.jar")) We provide the following Java libraries: Apache Commons Logging under the Apache License, Version 2.0, January 2004, http://commons. apache.org/logging/, Copyright 2001-2007 The Apache Software Foundation Apache jog4j under Apache License 2.0, http://logging.apache.org/log4j/, Copyright 2007 The Apache Software Foundation Apache Commons Lang under Apache License 2.0, http://commons.apache.org/lang/, Copyright 2001-2011 The Apache Software Foundation Apache POI under Apache License 2.0, http://poi.apache.org/, Copyright 2001-2007 The Apache Software Foundation Apache Commons Collections under the Apache License, Version 2.0, January 2004, http://commons. apache.org/collections/, Copyright 2001-2008 The Apache Software Foundation Apache Commons Validator under the Apache License, Version 2.0, January 2004, http://commons. apache.org/validator/, Copyright 2001-2010 The Apache Software Foundation JLaTeXMath under GPL >= 2.0, http://forge.scilab.org/index.php/p/jlatexmath/, Copyright 2004-2007, 2009 Calixte, Coolsaet, Cleemput, Vermeulen and Universiteit Gent iText 2.1.4 under LGPL, http://itextpdf.com/, Copyright -
Java Linksammlung
JAVA LINKSAMMLUNG LerneProgrammieren.de - 2020 Java einfach lernen (klicke hier) JAVA LINKSAMMLUNG INHALTSVERZEICHNIS Build ........................................................................................................................................................... 4 Caching ....................................................................................................................................................... 4 CLI ............................................................................................................................................................... 4 Cluster-Verwaltung .................................................................................................................................... 5 Code-Analyse ............................................................................................................................................. 5 Code-Generators ........................................................................................................................................ 5 Compiler ..................................................................................................................................................... 6 Konfiguration ............................................................................................................................................. 6 CSV ............................................................................................................................................................. 6 Daten-Strukturen -
Analysis of Big Data Storage Tools for Data Lakes Based on Apache Hadoop Platform
(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 12, No. 8, 2021 Analysis of Big Data Storage Tools for Data Lakes based on Apache Hadoop Platform Vladimir Belov, Evgeny Nikulchev MIREA—Russian Technological University, Moscow, Russia Abstract—When developing large data processing systems, determined the emergence and use of various data storage the question of data storage arises. One of the modern tools for formats in HDFS. solving this problem is the so-called data lakes. Many implementations of data lakes use Apache Hadoop as a basic Among the most widely known formats used in the Hadoop platform. Hadoop does not have a default data storage format, system are JSON [12], CSV [13], SequenceFile [14], Apache which leads to the task of choosing a data format when designing Parquet [15], ORC [16], Apache Avro [17], PBF [18]. a data processing system. To solve this problem, it is necessary to However, this list is not exhaustive. Recently, new formats of proceed from the results of the assessment according to several data storage are gaining popularity, such as Apache Hudi [19], criteria. In turn, experimental evaluation does not always give a Apache Iceberg [20], Delta Lake [21]. complete understanding of the possibilities for working with a particular data storage format. In this case, it is necessary to Each of these file formats has own features in file structure. study the features of the format, its internal structure, In addition, differences are observed at the level of practical recommendations for use, etc. The article describes the features application. Thus, row-oriented formats ensure high writing of both widely used data storage formats and the currently speed, but column-oriented formats are better for data reading. -
Declarative Languages for Big Streaming Data a Database Perspective
Tutorial Declarative Languages for Big Streaming Data A database Perspective Riccardo Tommasini Sherif Sakr University of Tartu Unversity of Tartu [email protected] [email protected] Emanuele Della Valle Hojjat Jafarpour Politecnico di Milano Confluent Inc. [email protected] [email protected] ABSTRACT sources and are pushed asynchronously to servers which are The Big Data movement proposes data streaming systems to responsible for processing them [13]. tame velocity and to enable reactive decision making. However, To facilitate the adoption, initially, most of the big stream approaching such systems is still too complex due to the paradigm processing systems provided their users with a set of API for shift they require, i.e., moving from scalable batch processing to implementing their applications. However, recently, the need for continuous data analysis and pattern detection. declarative stream processing languages has emerged to simplify Recently, declarative Languages are playing a crucial role in common coding tasks; making code more readable and main- fostering the adoption of Stream Processing solutions. In partic- tainable, and fostering the development of more complex appli- ular, several key players introduce SQL extensions for stream cations. Thus, Big Data frameworks (e.g., Flink [9], Spark [3], 1 processing. These new languages are currently playing a cen- Kafka Streams , and Storm [19]) are starting to develop their 2 3 4 tral role in fostering the stream processing paradigm shift. In own SQL-like approaches (e.g., Flink SQL , Beam SQL , KSQL ) this tutorial, we give an overview of the various languages for to declaratively tame data velocity. declarative querying interfaces big streaming data. -
Administration and Configuration Guide
Red Hat JBoss Data Virtualization 6.4 Administration and Configuration Guide This guide is for administrators. Last Updated: 2018-09-26 Red Hat JBoss Data Virtualization 6.4 Administration and Configuration Guide This guide is for administrators. Red Hat Customer Content Services Legal Notice Copyright © 2018 Red Hat, Inc. This document is licensed by Red Hat under the Creative Commons Attribution-ShareAlike 3.0 Unported License. If you distribute this document, or a modified version of it, you must provide attribution to Red Hat, Inc. and provide a link to the original. If the document is modified, all Red Hat trademarks must be removed. Red Hat, as the licensor of this document, waives the right to enforce, and agrees not to assert, Section 4d of CC-BY-SA to the fullest extent permitted by applicable law. Red Hat, Red Hat Enterprise Linux, the Shadowman logo, JBoss, OpenShift, Fedora, the Infinity logo, and RHCE are trademarks of Red Hat, Inc., registered in the United States and other countries. Linux ® is the registered trademark of Linus Torvalds in the United States and other countries. Java ® is a registered trademark of Oracle and/or its affiliates. XFS ® is a trademark of Silicon Graphics International Corp. or its subsidiaries in the United States and/or other countries. MySQL ® is a registered trademark of MySQL AB in the United States, the European Union and other countries. Node.js ® is an official trademark of Joyent. Red Hat Software Collections is not formally related to or endorsed by the official Joyent Node.js open source or commercial project. -
The Pentaho Big Data Guide This Document Supports Pentaho Business Analytics Suite 4.8 GA and Pentaho Data Integration 4.4 GA, Documentation Revision October 31, 2012
The Pentaho Big Data Guide This document supports Pentaho Business Analytics Suite 4.8 GA and Pentaho Data Integration 4.4 GA, documentation revision October 31, 2012. This document is copyright © 2012 Pentaho Corporation. No part may be reprinted without written permission from Pentaho Corporation. All trademarks are the property of their respective owners. Help and Support Resources If you have questions that are not covered in this guide, or if you would like to report errors in the documentation, please contact your Pentaho technical support representative. Support-related questions should be submitted through the Pentaho Customer Support Portal at http://support.pentaho.com. For information about how to purchase support or enable an additional named support contact, please contact your sales representative, or send an email to [email protected]. For information about instructor-led training on the topics covered in this guide, visit http://www.pentaho.com/training. Limits of Liability and Disclaimer of Warranty The author(s) of this document have used their best efforts in preparing the content and the programs contained in it. These efforts include the development, research, and testing of the theories and programs to determine their effectiveness. The author and publisher make no warranty of any kind, express or implied, with regard to these programs or the documentation contained in this book. The author(s) and Pentaho shall not be liable in the event of incidental or consequential damages in connection with, or arising out of, the furnishing, performance, or use of the programs, associated instructions, and/or claims. Trademarks Pentaho (TM) and the Pentaho logo are registered trademarks of Pentaho Corporation. -
Oracle Metadata Management V12.2.1.3.0 New Features Overview
An Oracle White Paper October 12 th , 2018 Oracle Metadata Management v12.2.1.3.0 New Features Overview Oracle Metadata Management version 12.2.1.3.0 – October 12 th , 2018 New Features Overview Disclaimer This document is for informational purposes. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described in this document remains at the sole discretion of Oracle. This document in any form, software or printed matter, contains proprietary information that is the exclusive property of Oracle. This document and information contained herein may not be disclosed, copied, reproduced, or distributed to anyone outside Oracle without prior written consent of Oracle. This document is not part of your license agreement nor can it be incorporated into any contractual agreement with Oracle or its subsidiaries or affiliates. 1 Oracle Metadata Management version 12.2.1.3.0 – October 12 th , 2018 New Features Overview Table of Contents Executive Overview ............................................................................ 3 Oracle Metadata Management 12.2.1.3.0 .......................................... 4 METADATA MANAGER VS METADATA EXPLORER UI .............. 4 METADATA HOME PAGES ........................................................... 5 METADATA QUICK ACCESS ........................................................ 6 METADATA REPORTING ............................................................. -
Apache Apex: Next Gen Big Data Analytics
Apache Apex: Next Gen Big Data Analytics Thomas Weise <[email protected]> @thweise PMC Chair Apache Apex, Architect DataTorrent Apache Big Data Europe, Sevilla, Nov 14th 2016 Stream Data Processing Data Delivery Transform / Analytics Real-time visualization, … Declarative SQL API Data Beam Beam SAMOA Operator SAMOA DAG API Sources Library Events Logs Oper1 Oper2 Oper3 Sensor Data Social Databases CDC (roadmap) 2 Industries & Use Cases Financial Services Ad-Tech Telecom Manufacturing Energy IoT Real-time Call detail record customer facing (CDR) & Supply chain Fraud and risk Smart meter Data ingestion dashboards on extended data planning & monitoring analytics and processing key performance record (XDR) optimization indicators analysis Understanding Reduce outages Credit risk Click fraud customer Preventive & improve Predictive assessment detection behavior AND maintenance resource analytics context utilization Packaging and Improve turn around Asset & Billing selling Product quality & time of trade workforce Data governance optimization anonymous defect tracking settlement processes management customer data HORIZONTAL • Large scale ingest and distribution • Enforcing data quality and data governance requirements • Real-time ELTA (Extract Load Transform Analyze) • Real-time data enrichment with reference data • Dimensional computation & aggregation • Real-time machine learning model scoring 3 Apache Apex • In-memory, distributed stream processing • Application logic broken into components (operators) that execute distributed in a cluster • -
Synthesis and Development of a Big Data Architecture for the Management of Radar Measurement Data
1 Faculty of Electrical Engineering, Mathematics & Computer Science Synthesis and Development of a Big Data architecture for the management of radar measurement data Alex Aalbertsberg Master of Science Thesis November 2018 Supervisors: dr. ir. Maurice van Keulen (University of Twente) prof. dr. ir. Mehmet Aks¸it (University of Twente) dr. Doina Bucur (University of Twente) ir. Ronny Harmanny (Thales) University of Twente P.O. Box 217 7500 AE Enschede The Netherlands Approval Internship report/Thesis of: …………………………………………………………………………………………………………Alexander P. Aalbertsberg Title: …………………………………………………………………………………………Synthesis and Development of a Big Data architecture for the management of radar measurement data Educational institution: ………………………………………………………………………………..University of Twente Internship/Graduation period:…………………………………………………………………………..2017-2018 Location/Department:.…………………………………………………………………………………435 Advanced Development, Delft Thales Supervisor:……………………………………………………………………………R. I. A. Harmanny This report (both the paper and electronic version) has been read and commented on by the supervisor of Thales Netherlands B.V. In doing so, the supervisor has reviewed the contents and considering their sensitivity, also information included therein such as floor plans, technical specifications, commercial confidential information and organizational charts that contain names. Based on this, the supervisor has decided the following: o This report is publicly available (Open). Any defence may take place publicly and the report may be included in public libraries and/or published in knowledge bases. • o This report and/or a summary thereof is publicly available to a limited extent (Thales Group Internal). tors . It will be read and reviewed exclusively by teachers and if necessary by members of the examination board or review ? committee. The content will be kept confidential and not disseminated through publication or inclusion in public libraries and/or knowledge bases. -
Developer Tool Guide
Informatica® 10.2.1 Developer Tool Guide Informatica Developer Tool Guide 10.2.1 May 2018 © Copyright Informatica LLC 2009, 2019 This software and documentation are provided only under a separate license agreement containing restrictions on use and disclosure. No part of this document may be reproduced or transmitted in any form, by any means (electronic, photocopying, recording or otherwise) without prior consent of Informatica LLC. Informatica, the Informatica logo, PowerCenter, and PowerExchange are trademarks or registered trademarks of Informatica LLC in the United States and many jurisdictions throughout the world. A current list of Informatica trademarks is available on the web at https://www.informatica.com/trademarks.html. Other company and product names may be trade names or trademarks of their respective owners. U.S. GOVERNMENT RIGHTS Programs, software, databases, and related documentation and technical data delivered to U.S. Government customers are "commercial computer software" or "commercial technical data" pursuant to the applicable Federal Acquisition Regulation and agency-specific supplemental regulations. As such, the use, duplication, disclosure, modification, and adaptation is subject to the restrictions and license terms set forth in the applicable Government contract, and, to the extent applicable by the terms of the Government contract, the additional rights set forth in FAR 52.227-19, Commercial Computer Software License. Portions of this software and/or documentation are subject to copyright held by third parties. Required third party notices are included with the product. The information in this documentation is subject to change without notice. If you find any problems in this documentation, report them to us at [email protected]. -
Quick Install for AWS EMR
Quick Install for AWS EMR Version: 6.8 Doc Build Date: 01/21/2020 Copyright © Trifacta Inc. 2020 - All Rights Reserved. CONFIDENTIAL These materials (the “Documentation”) are the confidential and proprietary information of Trifacta Inc. and may not be reproduced, modified, or distributed without the prior written permission of Trifacta Inc. EXCEPT AS OTHERWISE PROVIDED IN AN EXPRESS WRITTEN AGREEMENT, TRIFACTA INC. PROVIDES THIS DOCUMENTATION AS-IS AND WITHOUT WARRANTY AND TRIFACTA INC. DISCLAIMS ALL EXPRESS AND IMPLIED WARRANTIES TO THE EXTENT PERMITTED, INCLUDING WITHOUT LIMITATION THE IMPLIED WARRANTIES OF MERCHANTABILITY, NON-INFRINGEMENT AND FITNESS FOR A PARTICULAR PURPOSE AND UNDER NO CIRCUMSTANCES WILL TRIFACTA INC. BE LIABLE FOR ANY AMOUNT GREATER THAN ONE HUNDRED DOLLARS ($100) BASED ON ANY USE OF THE DOCUMENTATION. For third-party license information, please select About Trifacta from the Help menu. 1. Release Notes . 4 1.1 Changes to System Behavior . 4 1.1.1 Changes to the Language . 4 1.1.2 Changes to the APIs . 18 1.1.3 Changes to Configuration 23 1.1.4 Changes to the Object Model . 26 1.2 Release Notes 6.8 . 30 1.3 Release Notes 6.4 . 36 1.4 Release Notes 6.0 . 42 1.5 Release Notes 5.1 . 49 2. Quick Start 55 2.1 Install from AWS Marketplace with EMR . 55 2.2 Upgrade for AWS Marketplace with EMR . 62 3. Configure 62 3.1 Configure for AWS . 62 3.1.1 Configure for EC2 Role-Based Authentication . 68 3.1.2 Enable S3 Access . 70 3.1.2.1 Create Redshift Connections 81 3.1.3 Configure for EMR .