Trelational and DPS
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Oracle Database Gateway for Adabas User's Guide, 11G Release 2 (11.2) E12074-01
Oracle® Database Gateway for Adabas User’s Guide 11g Release 2 (11.2) E12074-01 July 2009 Oracle Database Gateway for Adabas User's Guide, 11g Release 2 (11.2) E12074-01 Copyright © 2008, 2009, Oracle and/or its affiliates. All rights reserved. Primary Author: Jeanne Wiegelmann Contributing Author: Maitreyee Chaliha, Sami Zeitoun, Oussama Mkaabal Contributor: Vira Goorah, Peter Wong 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 software or related documentation is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, the following notice is applicable: 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 shall be 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 (December 2007). -
Powerdesigner 16.6 Data Modeling
SAP® PowerDesigner® Document Version: 16.6 – 2016-02-22 Data Modeling Content 1 Building Data Models ...........................................................8 1.1 Getting Started with Data Modeling...................................................8 Conceptual Data Models........................................................8 Logical Data Models...........................................................9 Physical Data Models..........................................................9 Creating a Data Model.........................................................10 Customizing your Modeling Environment........................................... 15 1.2 Conceptual and Logical Diagrams...................................................26 Supported CDM/LDM Notations.................................................27 Conceptual Diagrams.........................................................31 Logical Diagrams............................................................43 Data Items (CDM)............................................................47 Entities (CDM/LDM)..........................................................49 Attributes (CDM/LDM)........................................................55 Identifiers (CDM/LDM)........................................................58 Relationships (CDM/LDM)..................................................... 59 Associations and Association Links (CDM)..........................................70 Inheritances (CDM/LDM)......................................................77 1.3 Physical Diagrams..............................................................82 -
Paper Mainframe
Club des Responsables d’Infrastructures et de Production IT Infrastructure and Operations IT INFRAsTRucTuRE & Operations MANAgEMENT Best Practices PAPER MAINFRAME IBM zSeries Mainframe Cost Control te i Authors Laurent Buscaylet, Frédéric Didier Bernard Dietisheim, Bruno Koch, Fabrice Vallet sponsored by september 2012 Wh PAgE 2 table of contents INTRODucTION 5 1. “z” POsITIONINg AND Strategy wIThIN A Company 6 1.1 Role of the Mainframe in the eyes of cRIP’s Members 6 1.2 The Position of system z vs. Distributed systems (windows, uNIX, Linux) 6 1.3 system z - Banking/Finance/Insurance vs. general Industry sectors 7 1.4 Evolution strategies for system z 9 2. z Platform OVERVIEw 11 2.1 Technical terms of reference 11 2.1.1 Servers 11 2.1.2 SAN & Networks 12 2.1.3 Storage 13 2.1.4 Backups 15 2.1.5 technical Architecture & Organization 16 2.1.6 Business Continuity 19 2.2 Mainframe software vendors (IsV’s) analysis & positioning 20 2.2.1 the Main Suppliers (ISV’s) 22 2.2.2 the Secondary Suppliers (ISV’s) 22 2.3 survey Results 3. z PLATFORM cOsT cONTROL 28 3.1 controling software cost 28 3.1.1 IBM Contractual Arrangements 28 3.1.2 ISV negotiation strategies: holistic/generic or piecemeal/ad-hoc 31 3.1.3 ISV contractual arrangements: licensing modes & billing 35 3.1.4 MLC: Controlling the billing level 38 3.1.4.1 The IBM billing method 38 3.1.4.2 The problem of smoothing (consolidating) peak loads 41 3.1.4.3 The methods and controls for controlling the software invoice 42 3.1.4.4 Survey feedback and recommendations 43 3.1.4.5 Other Cost Saving Options 48 3.2 Infrastructure cost Reduction 50 3.2.1 Grouping and Sharing Infrastructure 50 3.2.2 Use of specialty engines: zIIP, zAAP, IFL 54 3.3 controlling operational costs 55 3.3.1 Optimization, performance & technical/application component quality 55 3.3.2 Capacity Management, tools & methods 58 4. -
Denormalization Strategies for Data Retrieval from Data Warehouses
Decision Support Systems 42 (2006) 267–282 www.elsevier.com/locate/dsw Denormalization strategies for data retrieval from data warehouses Seung Kyoon Shina,*, G. Lawrence Sandersb,1 aCollege of Business Administration, University of Rhode Island, 7 Lippitt Road, Kingston, RI 02881-0802, United States bDepartment of Management Science and Systems, School of Management, State University of New York at Buffalo, Buffalo, NY 14260-4000, United States Available online 20 January 2005 Abstract In this study, the effects of denormalization on relational database system performance are discussed in the context of using denormalization strategies as a database design methodology for data warehouses. Four prevalent denormalization strategies have been identified and examined under various scenarios to illustrate the conditions where they are most effective. The relational algebra, query trees, and join cost function are used to examine the effect on the performance of relational systems. The guidelines and analysis provided are sufficiently general and they can be applicable to a variety of databases, in particular to data warehouse implementations, for decision support systems. D 2004 Elsevier B.V. All rights reserved. Keywords: Database design; Denormalization; Decision support systems; Data warehouse; Data mining 1. Introduction houses as issues related to database design for high performance are receiving more attention. Database With the increased availability of data collected design is still an art that relies heavily on human from the Internet and other sources and the implemen- intuition and experience. Consequently, its practice is tation of enterprise-wise data warehouses, the amount becoming more difficult as the applications that data- of data that companies possess is growing at a bases support become more sophisticated [32].Cur- phenomenal rate. -
A Comprehensive Analysis of Sybase Powerdesigner 16.0
white paper A Comprehensive Analysis of Sybase® PowerDesigner® 16.0 InformationArchitect vs. ER/Studio XE2 Version 2.0 www.sybase.com TABLe OF CONTENtS 1 Introduction 1 Product Overviews 1 ER/Studio XE2 3 Sybase PowerDesigner 16.0 4 Data Modeling Activities 4 Overview 6 Types of Data Model 7 Design Layers 8 Managing the SAM-LDM Relationship 10 Forward and Reverse Engineering 11 Round-trip Engineering 11 Integrating Data Models with Requirements and Processes 11 Generating Object-oriented Models 11 Dependency Analysis 17 Model Comparisons and Merges 18 Update Flows 18 Required Features for a Data Modeling Tool 18 Core Modeling 25 Collaboration 27 Interfaces & Integration 29 Usability 34 Managing Models as a Project 36 Dependency Matrices 37 Conclusions 37 Acknowledgements 37 Bibliography 37 About the Author IntrOduCtion Data modeling is more than just database design, because data doesn’t just exist in databases. Data does not exist in isolation, it is created, managed and consumed by business processes, and those business processes are implemented using a variety of applications and technologies. To truly understand and manage our data, and the impact of changes to that data, we need to manage more than just models of data in databases. We need support for different types of data models, and for managing the relationships between data and the rest of the organization. When you need to manage a data center across the enterprise, integrating with a wider set of business and technology activities is critical to success. For this reason, this review will use the InformationArchitect version of Sybase PowerDesigner rather than their DataArchitect™ version. -
Database Normalization
Outline Data Redundancy Normalization and Denormalization Normal Forms Database Management Systems Database Normalization Malay Bhattacharyya Assistant Professor Machine Intelligence Unit and Centre for Artificial Intelligence and Machine Learning Indian Statistical Institute, Kolkata February, 2020 Malay Bhattacharyya Database Management Systems Outline Data Redundancy Normalization and Denormalization Normal Forms 1 Data Redundancy 2 Normalization and Denormalization 3 Normal Forms First Normal Form Second Normal Form Third Normal Form Boyce-Codd Normal Form Elementary Key Normal Form Fourth Normal Form Fifth Normal Form Domain Key Normal Form Sixth Normal Form Malay Bhattacharyya Database Management Systems These issues can be addressed by decomposing the database { normalization forces this!!! Outline Data Redundancy Normalization and Denormalization Normal Forms Redundancy in databases Redundancy in a database denotes the repetition of stored data Redundancy might cause various anomalies and problems pertaining to storage requirements: Insertion anomalies: It may be impossible to store certain information without storing some other, unrelated information. Deletion anomalies: It may be impossible to delete certain information without losing some other, unrelated information. Update anomalies: If one copy of such repeated data is updated, all copies need to be updated to prevent inconsistency. Increasing storage requirements: The storage requirements may increase over time. Malay Bhattacharyya Database Management Systems Outline Data Redundancy Normalization and Denormalization Normal Forms Redundancy in databases Redundancy in a database denotes the repetition of stored data Redundancy might cause various anomalies and problems pertaining to storage requirements: Insertion anomalies: It may be impossible to store certain information without storing some other, unrelated information. Deletion anomalies: It may be impossible to delete certain information without losing some other, unrelated information. -
Installation for Z/OS
Natural Installation for z/OS Version 8.2.3 für Großrechner November 2012 Dieses Dokument gilt für Natural ab Version 8.2.3 für Großrechner. Hierin enthaltene Beschreibungen unterliegen Änderungen und Ergänzungen, die in nachfolgenden Release Notes oder Neuausgaben bekanntgegeben werden. Copyright © 1979-2012 Software AG, Darmstadt, Deutschland und/oder Software AG USA, Inc., Reston, VA, Vereinigte Staaten von Amerika, und/oder ihre Lizenzgeber.. Nähere Informationen zu den Patenten und Marken der Software AG und ihrer Tochtergesellschaften befinden sich unter http://documentation.softwareag.com/legal/. Die Nutzung dieser Software unterliegt den Lizenzbedingungen der Software AG. Diese Bedingungen sind Bestandteil der Produkt- dokumentation und befinden sich unter http://documentation.softwareag.com/legal/ und/oder im Wurzelverzeichnis des lizensierten Produkts. Diese Software kann Teile von Drittanbieterprodukten enthalten. Die Hinweise zu den Urheberrechten und Lizenzbedingungen der Drittanbieter entnehmen Sie bitte den "License Texts, Copyright Notices and Disclaimers of Third Party Products". Dieses Dokument ist Bestandteil der Produktdokumentation und befindet sich unter http://documentation.softwareag.com/legal/ und/oder im Wurzelverzeichnis des lizensierten Produkts. Dokument-ID: NATMF-INSTALL-ZOS-823-20121108 Table of Contents Preface ............................................................................................................................... ix I Installation Process and Major Natural Features on z/OS .............................................. -
Normalization for Relational Databases
Chapter 10 Functional Dependencies and Normalization for Relational Databases Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Chapter Outline 1 Informal Design Guidelines for Relational Databases 1.1Semantics of the Relation Attributes 1.2 Redundant Information in Tuples and Update Anomalies 1.3 Null Values in Tuples 1.4 Spurious Tuples 2. Functional Dependencies (skip) Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide 10- 2 Chapter Outline 3. Normal Forms Based on Primary Keys 3.1 Normalization of Relations 3.2 Practical Use of Normal Forms 3.3 Definitions of Keys and Attributes Participating in Keys 3.4 First Normal Form 3.5 Second Normal Form 3.6 Third Normal Form 4. General Normal Form Definitions (For Multiple Keys) 5. BCNF (Boyce-Codd Normal Form) Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide 10- 3 Informal Design Guidelines for Relational Databases (2) We first discuss informal guidelines for good relational design Then we discuss formal concepts of functional dependencies and normal forms - 1NF (First Normal Form) - 2NF (Second Normal Form) - 3NF (Third Normal Form) - BCNF (Boyce-Codd Normal Form) Additional types of dependencies, further normal forms, relational design algorithms by synthesis are discussed in Chapter 11 Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide 10- 4 1 Informal Design Guidelines for Relational Databases (1) What is relational database design? The grouping of attributes to form "good" relation schemas Two levels of relation schemas The logical "user view" level The storage "base relation" level Design is concerned mainly with base relations What are the criteria for "good" base relations? Copyright © 2007 Ramez Elmasri and Shamkant B. -
The Denormalized Relational Schema
The Denormalized Relational Schema How undying is Forster when take-out and wifely Hermon debauches some nebulisers? Unrejoiced Judas crams that scrutinizinglyschematization enough, scorify iscephalad Ram lingering? and verdigris substantivally. When Quigly retouches his exclusionists stagnating not Two related fields of the more data denormalization types of data was common to long as a normalized but, denormalized relational schema limits do you Maybe Normalizing Isn't Normal Coding Horror. Once she is told that this is a different animal called a cow, she will modify her existing schema for a horse and create a new schema for a cow. Overall these represent things that can be done at different stages in the design process that will maximize efficiencies of the model. Data redundancy leads to data anomalies and corruption and should be avoided when creating a relational database consisting of several entities. DBMS processes must insure integrity and accuracy. But relational databases still remain the default choice in most applications. That email is too long. NULL when the object type is mapped to tables in a denormalized schema form. Still, processing technology advancements have resulted in improved snowflake schema query performance in recent years, which is one of the reasons why snowflake schemas are rising in popularity. Updating, to the contrary, gets faster as all pieces of data are stored in a single place. Migration scripts are necessary. The reporting one is denormalized to get the most data in the most usable structure with each database call. Star schema dimension tables are not normalized, snowflake schemas dimension tables are normalized. -
Best Practices What It Means to Be A
What it means to be a DBA Best Practices Natural Conference in Boston, MA August 17-20, 2008 Dieter W. Storr [email protected] DBA ? Doing Business As …… Deutsche Ba (German airline) Doctor of Business Administration Davis-Bacon Act of 1931 Design Basis Accident Design Business Association Dual Band Antenna Direct Budget Authority Dollar Bill Acceptors Dumb But Adorable Danish Beekeepers' Association Dieter W. Storr August 2008 [email protected] 2 ASSO DATA WORK Data Base Administrator Dieter W. Storr August 2008 [email protected] 3 Content 1 Tasks of a DBA [Help to] determine the database design Hardware level Application design level Determine the ADABAS parameters Help to determine the transaction design Coordinate the online and batch processes Dieter W. Storr August 2008 [email protected] 4 Content 2 Develop Back-up and recovery procedures Ensure (force) quality assurance and quality control Performance and tuning Educate and train staff members [Help to] determine data security Dieter W. Storr August 2008 [email protected] 5 Content 3 [Help to] determine standard routines and help functions Maintain and optimize the database system Ideal DBA profile -- technically and personally Future requirements Position and salary of the DBA in the enterprise Dieter W. Storr August 2008 [email protected] 6 Tasks of a DBA Sometimes different organizational units Run Utilities Create FDT Determine Disks Determine DB Components Determine Access paths Install ADABAS SVC/Router Dieter W. Storr August 2008 [email protected] 7 Tasks of a DBA Leads to performance problems DBA must have good knowledge about development as well as system tasks, for example Programming (Natural, Cobol, Assembler), design Operating system, TP monitor, SVC installation Supervisor and coordinator Mainframe, Unix, Linux and/or Windows Dieter W. -
Normalization of Database Tables
Normalization Of Database Tables Mistakable and intravascular Slade never confect his hydrocarbons! Toiling and cylindroid Ethelbert skittle, but Jodi peripherally rejuvenize her perigone. Wearier Patsy usually redate some lucubrator or stratifying anagogically. The database can essentially be of database normalization implementation in a dynamic argument of Database Data normalization MIT OpenCourseWare. How still you structure a normlalized database you store receipt data? Draw data warehouse information will be familiar because it? Today, inventory is hardware key and database normalization. Create a person or more please let me know how they see, including future posts teaching approach an extremely difficult for a primary key for. Each invoice number is assigned a date of invoicing and a customer number. Transform the data into a format more suitable for analysis. Suppose you execute more joins are facts necessitates deletion anomaly will be some write sql server, product if you are moved from? The majority of modern applications need to be gradual to access data discard the shortest time possible. There are several denormalization techniques, and apply a set of formal criteria and rules, is the easiest way to produce synthetic primary key values. In a database performance have only be a candidate per master. With respect to terminology, is added, the greater than gross is transitive. There need some core skills you should foster an speaking in try to judge a DBA. Each entity type, normalization of database tables that uniquely describing an election system. Say that of contents. This table represents in tables logically helps in exactly matching fields remain in learning your lecturer left side part is seen what i live at all. -
Tcvision IBM Mainframe Integration Through Change Data Capture Fact Sheet
tcVISION IBM Mainframe Integration Through Change Data Capture Fact Sheet Mainframe data integration has taken on more urgency in recent years The tcVISION Solution as organizations seek to relocate mainframe workloads to lower-cost tcVISION is ready to meet new technologies and challenges. Thanks to platforms, modernize mainframe applications and leverage analytics for tcVISION’s flexible architecture, support for new targets—including AWS, customer insight and competitive advantage. These factors are driving specialty, NoSQL and analytic databases such as Exasol, IBM DB2 BLU adoption of Cloud (e.g., Amazon Web Services [AWS]) and Big Data as and MongoDB—, transport layers and protocols is being continuously strategic components in corporate technology architecture. added, quickly and with minimal effort. With tcVISION, real-time Cloud and Big Data integration can embrace both mainframe (IBM DB2, IMS/ tcVISION’s support for Cloud and Big Data as targets is fully integrated DB, DL/1, Software AG Adabas, CA IDMS, CA Datacom and alongside traditional Linux/Unix/Windows (LUW) targets such as Oracle VSAM), Cloud, and LUW (IBM DB2 LUW, Oracle, IBM Informix, Database, IBM DB2 LUW, Software AG Adabas LUW, IBM Informix, Sybase, Microsoft SQL Server, PostgreSQL, Software AG Adabas Sybase, Microsoft SQL Server, PostgreSQL and ODBC. LUW) sources. tcVISION can deliver replicated data to Cloud and Big Data targets Why tcVISION? through a variety of means: creating files, writing directly into Hadoop • Increasing number of enterprise applications that utilize their own HDFS, and via streaming using Apache Kafka as the transport layer. Data databases. can be packaged using standard JSON and CSV protocols. • Requirement for up-to-date information demands real-time, bi- directional data synchronization between mainframe and open systems.