EMC Data Computing Appliance 2 Getting Started Guide Software

Total Page:16

File Type:pdf, Size:1020Kb

EMC Data Computing Appliance 2 Getting Started Guide Software EMC® Data Computing Appliance Appliance Version 2 / Software Version 3.3.0.0 Getting Started Guide PART NUMBER: 302-004-091 REVISION: 01 Copyright © 2017 Dell Inc. or its subsidiaries. All rights reserved. Published June 2017 Dell believes the information in this publication is accurate as of its publication date. The information is subject to change without notice. THE INFORMATION IN THIS PUBLICATION IS PROVIDED “AS-IS.“ DELL MAKES NO REPRESENTATIONS OR WARRANTIES OF ANY KIND WITH RESPECT TO THE INFORMATION IN THIS PUBLICATION, AND SPECIFICALLY DISCLAIMS IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. USE, COPYING, AND DISTRIBUTION OF ANY DELL SOFTWARE DESCRIBED IN THIS PUBLICATION REQUIRES AN APPLICABLE SOFTWARE LICENSE. Dell, EMC, and other trademarks are trademarks of Dell Inc. or its subsidiaries. Other trademarks may be the property of their respective owners. Published in the USA. EMC Corporation Hopkinton, Massachusetts 01748-9103 1-508-435-1000 In North America 1-866-464-7381 www.EMC.com Contents Preface ................................................................................................5 Welcome...........................................................................................5 About This Guide ..............................................................................5 Document Conventions .....................................................................6 Text Conventions.........................................................................6 Command Syntax Conventions ....................................................7 Getting Support ................................................................................7 Product information .....................................................................7 Technical support ........................................................................7 Chapter 1: About the DCA ..............................................................9 About the DCA ..................................................................................9 Two Appliance Versions ...............................................................9 DCA Module Types.......................................................................9 Racking Guidelines ....................................................................15 Rack Types................................................................................15 Rack Density .............................................................................17 About the Network Configuration...............................................18 DCA Modules and Master Servers....................................................20 Master Servers ..........................................................................21 GPDB Modules ...........................................................................21 Data Integration Accelerator Modules ........................................23 HD Compute Modules ................................................................26 Hadoop Master and Worker Modules..........................................27 GPDB Overview and Upgrade Tasks ................................................28 About GPDB...............................................................................28 About the Master Servers ..........................................................29 About the Segment Hosts..........................................................30 GPDB Upgrade Tasks.................................................................31 Chapter 2: Supported Software Applications .........................33 GPDB ..............................................................................................33 Pivotal Greenplum Command Center...............................................33 Pivotal Hadoop................................................................................33 HAWQ .............................................................................................34 Pivotal HD with EMC Isilon ..............................................................34 Pivotal Command Center.................................................................34 Supported software application versions .........................................34 Chapter 3: Preparing the Data Center Environment............. 37 Confirming Site Requirements.........................................................37 Floor Space Requirements .........................................................37 DCA Rack Dimensions ...............................................................38 Connecting New Racks to the Power Supply ..............................41 Power Cord Specifications..........................................................41 Environmental Requirements.....................................................41 Air Quality Requirements...........................................................42 Optional Securing Brackets .............................................................43 Anti-Tip Bracket ........................................................................44 Anti-Move Bracket .....................................................................44 Seismic Restraint Bracket..........................................................45 3 Cabinet Positioning .........................................................................46 Package Dimensions and Clearance ................................................47 Chapter 4: Planning for a Multiple Rack DCA .........................49 Chapter 5: Gathering Site-Specific Information ....................51 Site Requirements Checklist............................................................51 Plan for Hadoop Networking............................................................53 VLAN Overlay..................................................................................53 Planning for Remote Support - ESRS and Dialhome ........................54 Chapter 6: DCA Administration ..................................................57 DCA utilities ....................................................................................57 Description ................................................................................60 Options .....................................................................................61 ConnectEMC Dial Home Capability .............................................64 Web-Based Management Options ..............................................68 Pivotal Greenplum Command Center .........................................68 Pivotal Command Center ...........................................................68 GPDB Email and SNMP Alerting .................................................69 SNMP on the DCA ...........................................................................69 DCA MIB information ......................................................................69 MIB Locations............................................................................69 MIB Contents.............................................................................70 View MIB ...................................................................................72 Integrate DCA MIB with environment..............................................84 Change the SNMP community string..........................................84 Set an SNMP Trap Sink..............................................................84 General Database Maintenance Tasks .............................................85 Routine Vacuum and Analyze ....................................................85 Routine Reindexing....................................................................86 Managing GPDB Log Files ..........................................................86 Next Steps ......................................................................................87 Chapter 7: Power Down the DCA ...............................................89 Chapter 8: Next Steps ...................................................................95 Documentation Resources...............................................................95 Providing User Access to GPDB .......................................................95 Creating Databases and Loading Data.............................................95 Appendix A: Red Hat Enterprise Linux End User License Agreement ......................................................................................97 Glossary ............................................................................................99 4 EMC DCA Getting Started Guide Preface This guide is intended for EMC personnel, partners, database and system administrators, and customers to plan for installing a new Data Computing Appliance (DCA) into a data center. This guide provides an overview of the system, information on data center requirements, a checklist of items needed for software configuration, and links to relevant documentation for use in the next steps of deployment. This guide also contains an overview of the appliance configuration. Make sure that you verify that the requirements listed in this document are satisfied before performing a DCA installation. Welcome • About This Guide • Document Conventions • Getting Support • Welcome Welcome to EMC and congratulations on your new acquisition of your EMC DCA product. To help you get started as a new EMC Customer, please visit our online support Welcome Center at http://www.emc.com/support/new-customers/index.htm. Here, you will find information to help you gain access to the tools and resources
Recommended publications
  • Greenplum Database Performance on Vmware Vsphere 5.5
    Greenplum Database Performance on VMware vSphere 5.5 Performance Study TECHNICAL WHITEPAPER Greenplum Database Performance on VMware vSphere 5.5 Table of Contents Introduction................................................................................................................................................................................................................... 3 Experimental Configuration and Methodology ............................................................................................................................................ 3 Test Bed Configuration ..................................................................................................................................................................................... 3 Test and Measurement Tools ......................................................................................................................................................................... 5 Test Cases and Test Method ......................................................................................................................................................................... 6 Experimental Results ................................................................................................................................................................................................ 7 Performance Comparison: Physical to Virtual ......................................................................................................................................
    [Show full text]
  • An Overview of the 50 Most Common Web Scraping Tools
    AN OVERVIEW OF THE 50 MOST COMMON WEB SCRAPING TOOLS WEB SCRAPING IS THE PROCESS OF USING BOTS TO EXTRACT CONTENT AND DATA FROM A WEBSITE. UNLIKE SCREEN SCRAPING, WHICH ONLY COPIES PIXELS DISPLAYED ON SCREEN, WEB SCRAPING EXTRACTS UNDERLYING CODE — AND WITH IT, STORED DATA — AND OUTPUTS THAT INFORMATION INTO A DESIGNATED FILE FORMAT. While legitimate uses cases exist for data harvesting, illegal purposes exist as well, including undercutting prices and theft of copyrighted content. Understanding web scraping bots starts with understanding the diverse and assorted array of web scraping tools and existing platforms. Following is a high-level overview of the 50 most common web scraping tools and platforms currently available. PAGE 1 50 OF THE MOST COMMON WEB SCRAPING TOOLS NAME DESCRIPTION 1 Apache Nutch Apache Nutch is an extensible and scalable open-source web crawler software project. A-Parser is a multithreaded parser of search engines, site assessment services, keywords 2 A-Parser and content. 3 Apify Apify is a Node.js library similar to Scrapy and can be used for scraping libraries in JavaScript. Artoo.js provides script that can be run from your browser’s bookmark bar to scrape a website 4 Artoo.js and return the data in JSON format. Blockspring lets users build visualizations from the most innovative blocks developed 5 Blockspring by engineers within your organization. BotScraper is a tool for advanced web scraping and data extraction services that helps 6 BotScraper organizations from small and medium-sized businesses. Cheerio is a library that parses HTML and XML documents and allows use of jQuery syntax while 7 Cheerio working with the downloaded data.
    [Show full text]
  • Data Warehouse Fundamentals for Storage Professionals – What You Need to Know EMC Proven Professional Knowledge Sharing 2011
    Data Warehouse Fundamentals for Storage Professionals – What You Need To Know EMC Proven Professional Knowledge Sharing 2011 Bruce Yellin Advisory Technology Consultant EMC Corporation [email protected] Table of Contents Introduction ................................................................................................................................ 3 Data Warehouse Background .................................................................................................... 4 What Is a Data Warehouse? ................................................................................................... 4 Data Mart Defined .................................................................................................................. 8 Schemas and Data Models ..................................................................................................... 9 Data Warehouse Design – Top Down or Bottom Up? ............................................................10 Extract, Transformation and Loading (ETL) ...........................................................................11 Why You Build a Data Warehouse: Business Intelligence .....................................................13 Technology to the Rescue?.......................................................................................................19 RASP - Reliability, Availability, Scalability and Performance ..................................................20 Data Warehouse Backups .....................................................................................................26
    [Show full text]
  • Data and Computer Communications (Eighth Edition)
    DATA AND COMPUTER COMMUNICATIONS Eighth Edition William Stallings Upper Saddle River, New Jersey 07458 Library of Congress Cataloging-in-Publication Data on File Vice President and Editorial Director, ECS: Art Editor: Gregory Dulles Marcia J. Horton Director, Image Resource Center: Melinda Reo Executive Editor: Tracy Dunkelberger Manager, Rights and Permissions: Zina Arabia Assistant Editor: Carole Snyder Manager,Visual Research: Beth Brenzel Editorial Assistant: Christianna Lee Manager, Cover Visual Research and Permissions: Executive Managing Editor: Vince O’Brien Karen Sanatar Managing Editor: Camille Trentacoste Manufacturing Manager, ESM: Alexis Heydt-Long Production Editor: Rose Kernan Manufacturing Buyer: Lisa McDowell Director of Creative Services: Paul Belfanti Executive Marketing Manager: Robin O’Brien Creative Director: Juan Lopez Marketing Assistant: Mack Patterson Cover Designer: Bruce Kenselaar Managing Editor,AV Management and Production: Patricia Burns ©2007 Pearson Education, Inc. Pearson Prentice Hall Pearson Education, Inc. Upper Saddle River, NJ 07458 All rights reserved. No part of this book may be reproduced in any form or by any means, without permission in writing from the publisher. Pearson Prentice Hall™ is a trademark of Pearson Education, Inc. All other tradmarks or product names are the property of their respective owners. The author and publisher of this book have used their best efforts in preparing this book.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, expressed or implied, with regard to these programs or the documentation contained in this book.The author and publisher shall not be liable in any event for incidental or consequential damages in connection with, or arising out of, the furnishing, performance, or use of these programs.
    [Show full text]
  • Research Data Management Best Practices
    Research Data Management Best Practices Introduction ............................................................................................................................................................................ 2 Planning & Data Management Plans ...................................................................................................................................... 3 Naming and Organizing Your Files .......................................................................................................................................... 6 Choosing File Formats ............................................................................................................................................................. 9 Working with Tabular Data ................................................................................................................................................... 10 Describing Your Data: Data Dictionaries ............................................................................................................................... 12 Describing Your Project: Citation Metadata ......................................................................................................................... 15 Preparing for Storage and Preservation ............................................................................................................................... 17 Choosing a Repository .........................................................................................................................................................
    [Show full text]
  • Data Management, Analysis Tools, and Analysis Mechanics
    Chapter 2 Data Management, Analysis Tools, and Analysis Mechanics This chapter explores different tools and techniques for handling data for research purposes. This chapter assumes that a research problem statement has been formulated, research hypotheses have been stated, data collection planning has been conducted, and data have been collected from various sources (see Volume I for information and details on these phases of research). This chapter discusses how to combine and manage data streams, and how to use data management tools to produce analytical results that are error free and reproducible, once useful data have been obtained to accomplish the overall research goals and objectives. Purpose of Data Management Proper data handling and management is crucial to the success and reproducibility of a statistical analysis. Selection of the appropriate tools and efficient use of these tools can save the researcher numerous hours, and allow other researchers to leverage the products of their work. In addition, as the size of databases in transportation continue to grow, it is becoming increasingly important to invest resources into the management of these data. There are a number of ancillary steps that need to be performed both before and after statistical analysis of data. For example, a database composed of different data streams needs to be matched and integrated into a single database for analysis. In addition, in some cases data must be transformed into the preferred electronic format for a variety of statistical packages. Sometimes, data obtained from “the field” must be cleaned and debugged for input and measurement errors, and reformatted. The following sections discuss considerations for developing an overall data collection, handling, and management plan, and tools necessary for successful implementation of that plan.
    [Show full text]
  • EMC Secure Remote Services 3.18 Site Planning Guide
    EMC® Secure Remote Services Release 3.26 Site Planning Guide REV 01 Copyright © 2018 EMC Corporation. All rights reserved. Published in the USA. Published January 2018 EMC believes the information in this publication is accurate as of its publication date. The information is subject to change without notice. The information in this publication is provided as is. EMC Corporation makes no representations or warranties of any kind with respect to the information in this publication, and specifically disclaims implied warranties of merchantability or fitness for a particular purpose. Use, copying, and distribution of any EMC software described in this publication requires an applicable software license. EMC2, EMC, and the EMC logo are registered trademarks or trademarks of EMC Corporation in the United States and other countries. All other trademarks used herein are the property of their respective owners. For the most up-to-date regulatory document for your product line, go to Dell EMC Online Support (https://support.emc.com). 2 EMC Secure Remote Services Site Planning Guide CONTENTS Preface Chapter 1 Overview ESRS architecture........................................................................................ 10 ESRS installation options ...................................................................... 10 Other components ................................................................................ 11 Requirements for ESRS customers......................................................... 11 Supported devices.....................................................................................
    [Show full text]
  • Computer Files & Data Storage
    STORAGE & FILE CONCEPTS, UTILITIES (Pages 6, 150-158 - Discovering Computers & Microsoft Office 2010) I. Computer files – data, information or instructions residing on secondary storage are stored in the form of a file. A. Software files are also called program files. Program files (instructions) are created by a computer programmer and generally cannot be modified by a user. It’s important that we not move or delete program files because your computer requires them to perform operations. Program files are also referred to as “executables”. 1. You can identify a program file by its extension:“.EXE”, “.COM”, “.BAT”, “.DLL”, “.SYS”, or “.INI” (there are others) or a distinct program icon. B. Data files - when you select a “save” option while using an application program, you are in essence creating a data file. Users create data files. 1. File naming conventions refer to the guidelines followed while assigning file names and will vary with the operating system and application in use (see figure 4-1). File names in Windows 7 may be up to 255 characters, you're not allowed to use reserved characters or certain reserved words. File extensions are used to identify the application that was used to create the file and format data in a manner recognized by the source application used to create it. FALL 2012 1 II. Selecting secondary storage media A. There are three type of technologies for storage devices: magnetic, optical, & solid state, there are advantages & disadvantages between them. When selecting a secondary storage device, certain factors should be considered: 1. Capacity - the capacity of computer storage is expressed in bytes.
    [Show full text]
  • File Format Guidelines for Management and Long-Term Retention of Electronic Records
    FILE FORMAT GUIDELINES FOR MANAGEMENT AND LONG-TERM RETENTION OF ELECTRONIC RECORDS 9/10/2012 State Archives of North Carolina File Format Guidelines for Management and Long-Term Retention of Electronic records Table of Contents 1. GUIDELINES AND RECOMMENDATIONS .................................................................................. 3 2. DESCRIPTION OF FORMATS RECOMMENDED FOR LONG-TERM RETENTION ......................... 7 2.1 Word Processing Documents ...................................................................................................................... 7 2.1.1 PDF/A-1a (.pdf) (ISO 19005-1 compliant PDF/A) ........................................................................ 7 2.1.2 OpenDocument Text (.odt) ................................................................................................................... 3 2.1.3 Special Note on Google Docs™ .......................................................................................................... 4 2.2 Plain Text Documents ................................................................................................................................... 5 2.2.1 Plain Text (.txt) US-ASCII or UTF-8 encoding ................................................................................... 6 2.2.2 Comma-separated file (.csv) US-ASCII or UTF-8 encoding ........................................................... 7 2.2.3 Tab-delimited file (.txt) US-ASCII or UTF-8 encoding .................................................................... 8 2.3
    [Show full text]
  • Overview of Data Format, Data Model and Procedural Metadata to Support Iot
    Overview of data format, data model and procedural metadata to support IoT Nakyoung Kim Scattered IoT ecosystem • Service and application–dedicated data formats & models • Data formats and models have been developed to suit the specific requirements of each industry, service, and application. • The current scattered-data ecosystem has been established, and it requires high costs to process and manage data for service and application convergence. • Needs of data integration and aggregation • The boundaries between industries are gradually crumbling down due to domain convergence. • For the future convergence markets of IoT and smart city, data integration and aggregation are important. Application-dedicated data management 2 Interoperability through the Web • Bridging the data • Data formats and models have been settled in the current forms over a period to resolve issues at each moment and fit case-by-case requirements. • Therefore, it is hardly possible to reformulate or replace the existing data formats and models. • Bridging the data formats and models is feasible. • Connecting via Web • A semantic information space for heterogeneous data, platforms, and application domains, Web can provide technologies that support the interoperability of IoT. • Enhanced interoperability in IoT ecosystems can be fulfilled with the concepts of Web of Things. Web Data-driven data management 3 Annotation and Microdata format • Structured data for annotation • To exploit what the Web offers, IoT data first needs to be structured and annotated just like how the other data is handled on the Web. • Microdata format • Microdata formats refer structured data markups describing and embedding the meanings of resources on the Web along with their properties and relationships.
    [Show full text]
  • Modular Data Storage with Anvil
    Modular Data Storage with Anvil Mike Mammarella Shant Hovsepian Eddie Kohler UCLA UCLA UCLA/Meraki [email protected] [email protected] [email protected] http://www.read.cs.ucla.edu/anvil/ ABSTRACT age strategies and behaviors. We intend Anvil configura- Databases have achieved orders-of-magnitude performance tions to serve as single-machine back-end storage layers for improvements by changing the layout of stored data – for databases and other structured data management systems. instance, by arranging data in columns or compressing it be- The basic Anvil abstraction is the dTable, an abstract key- fore storage. These improvements have been implemented value store. Some dTables communicate directly with sta- in monolithic new engines, however, making it difficult to ble storage, while others layer above storage dTables, trans- experiment with feature combinations or extensions. We forming their contents. dTables can represent row stores present Anvil, a modular and extensible toolkit for build- and column stores, but their fine-grained modularity of- ing database back ends. Anvil’s storage modules, called dTa- fers database designers more possibilities. For example, a bles, have much finer granularity than prior work. For ex- typical Anvil configuration splits a single “table” into sev- ample, some dTables specialize in writing data, while oth- eral distinct dTables, including a log to absorb writes and ers provide optimized read-only formats. This specialization read-optimized structures to satisfy uncached queries. This makes both kinds of dTable simple to write and understand. split introduces opportunities for clean extensibility – for Unifying dTables implement more comprehensive function- example, we present a Bloom filter dTable that can slot ality by layering over other dTables – for instance, building a above read-optimized stores and improve the performance read/write store from read-only tables and a writable journal, of nonexistent key lookup.
    [Show full text]
  • Mapping Approaches to Data and Data Flows
    www.oecd.org/innovation www.oecd.org/trade MAPPING APPROACHES @OECDinnovation @OECDtrade TO DATA AND [email protected] DATA FLOWS [email protected] Report for the G20 Digital Economy Task Force SAUDI ARABIA, 2020 This document was prepared by the Organisation for Economic Co-operation and Development (OECD) Directorate for Science, Technology and Innovation (STI) and Trade and Agriculture Directorate (TAD), as an input for the discussions in the G20 Digital Economy Task Force in 2020, under the auspices of the G20 Saudi Arabia Presidency in 2020. The opinions expressed and arguments employed herein do not necessarily represent the official views of the member countries of the OECD or the G20. This document and any map included herein are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area. Cover image: Jason Leung on Unsplash. © OECD 2020 The use of this work, whether digital or print, is governed by the Terms and Conditions to be found at http://www.oecd.org/termsandconditions. 2 © OECD 2020 Contents Executive Summary 4 1. Motivation and aim of the work 6 2. Context and scene setting 7 2.1. Data is an increasingly critical resource, generating benefits across society 7 2.2. The effective use of data boosts productivity and enables economic activity across all sectors8 2.3. The data ecosystem value chain is global 9 2.4. Data sharing enables increasing returns to scale and scope 10 2.5. Measures that affect data sharing and cross-border data flows can restrict the functioning of markets 11 2.6.
    [Show full text]