Teradata Solution Technical Overview
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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 -
Supporting Decision Making Chapter
Chapter 5 Supporting Decision Making McGraw-Hill/Irwin Copyright © 2008, The McGraw-Hill Companies, Inc. All rights reserved. Chapter Highlights • Introduction • Decision support systems • Management information systems • Online analytical processing • Using decision support systems • Executive information systems • Enterprise information portals • Knowledge management systems 2-2 Learning Objectives • Identify the changes taking place in the form and use of decision support in business. • Identify the role and reporting alternatives of management information systems. • Describe how online analytical processing can meet key information needs of managers. • Explain how the following IS can support the information needs of executives, managers and business professionals: a. Executives information systems b. Enterprise information portals c. Knowledge management systems 2-3 INTRODUCTION • To succeed in business today, companies need IS that can support the diverse information and decision making needs of their managers and business professionals. • Internet, Intranets and other Web enabled information technologies have significantly support the role that IS play in supporting the decision making activities of every managers and knowledge workers in business. 2-4 • The type of information required by decision makers in a company is directly related to the level of management decision making and the amount of structure in the decision situations they face. • Levels of managerial decision making that must be supported by information technology in a successful organization are: • Strategic management • Tactical management • Operational management 2-5 • Strategic Management • Board of directors and an executive committee of the CEO and top executives develop overall organizational goals, strategies, policies and objectives as part of a strategic planning process. • They also monitor the strategic performance of the organization and its overall direction in the political, economic and competitive business environment. -
Cluster Analysis for Olap in Online Decision Support Systems
Turkish Journal of Physiotherapy and Rehabilitation; 32(3) ISSN 2651-4451 | e-ISSN 2651-446X CLUSTER ANALYSIS FOR OLAP IN ONLINE DECISION SUPPORT SYSTEMS Kiruthika S1, Umamaheswari E2, Karmel A3, Kanchana Devi V4 1Department of Computer Science and Engineering, Sona College of Technology, Salem. [email protected] 2Associate Professor Grade - II, Center Faculty - Cyber Physical Systems, Vellore Institute of Technology, Chennai, 600 127, Tamilnadu, India [email protected] 3Associate Professor Grade - I, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600 127, Tamilnadu, India [email protected] 4Associate Professor Grade - I, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600 127, Tamilnadu, India [email protected] ABSTRACT Online Decision Support Systems serve people of different segments of the society viz., traders, business enthusiasts, entrepreneurs, etc., Decision Support Systems are used in almost every business segment, spanning from micro-level to High level and even Heavy industries. This phenomenon is one of the results of Globalization. Proper decisions have to be made in every business industry to cope up with the prevailing market conditions. These Decision support systems are fed with large volumes of data. These data volumes are generally huge and heterogeneous. Cluster Analysis is a technique used to find out the group of data which is similar to each other and dissimilar to other data. This technique is very important for the efficient functioning of any Online Decision Support System. This paper focuses on using the Cluster Analysis Techniques to make the Decision Support Systems to work efficiently. -
Master Data Management Whitepaper.Indd
Creating a Master Data Environment An Incremental Roadmap for Public Sector Organizations Master Data Management (MDM) is the processes and technologies used to create and maintain consistent and accurate representations of master data. Movements toward modularity, service orientation, and SaaS make Master Data Management a critical issue. Master Data Management leverages both tools and processes that enable the linkage of critical data through a unifi ed platform that provides a common point of reference. When properly done, MDM streamlines data management and sharing across the enterprise among different areas to provide more effective service delivery. CMA possesses more than 15 years of experience in Master Data Management and more than 20 in data management and integration. CMA has also focused our efforts on public sector since our inception, more than 30 years ago. This document describes the fundamental keys to success for developing and maintaining a long term master data program within a public service organization. www.cma.com 2 Understanding Master Data transactional data. As it becomes more volatile, it typically is considered more transactional. Simple Master Data Behavior entities are rarely a challenge to manage and are rarely Master data can be described by the way that it interacts considered master-data elements. The less complex an with other data. For example, in transaction systems, element, the less likely the need to manage change for master data is almost always involved with transactional that element. The more valuable the data element is to data. This relationship between master data and the organization, the more likely it will be considered a transactional data may be fundamentally viewed as a master data element. -
Data Warehouse Applications in Modern Day Business
California State University, San Bernardino CSUSB ScholarWorks Theses Digitization Project John M. Pfau Library 2002 Data warehouse applications in modern day business Carla Mounir Issa Follow this and additional works at: https://scholarworks.lib.csusb.edu/etd-project Part of the Business Intelligence Commons, and the Databases and Information Systems Commons Recommended Citation Issa, Carla Mounir, "Data warehouse applications in modern day business" (2002). Theses Digitization Project. 2148. https://scholarworks.lib.csusb.edu/etd-project/2148 This Project is brought to you for free and open access by the John M. Pfau Library at CSUSB ScholarWorks. It has been accepted for inclusion in Theses Digitization Project by an authorized administrator of CSUSB ScholarWorks. For more information, please contact [email protected]. DATA WAREHOUSE APPLICATIONS IN MODERN DAY BUSINESS A Project Presented to the Faculty of California State University, San Bernardino In Partial Fulfillment of the Requirements for the Degree Master of Business Administration by Carla Mounir Issa June 2002 DATA WAREHOUSE APPLICATIONS IN MODERN DAY BUSINESS A Project Presented to the Faculty of California State University, San Bernardino by Carla Mounir Issa June 2002 Approved by: Date Dr. Walter T. Stewart ABSTRACT Data warehousing is not a new concept in the business world. However, the constant changing application of data warehousing is what is being innovated constantly. It is these applications that are enabling managers to make better decisions through strategic planning, and allowing organizations to achieve a competitive advantage. The technology of implementing the data warehouse will help with the decision making process, analysis design, and will be more cost effective in the future. -
Lecture Notes on Data Mining& Data Warehousing
LECTURE NOTES ON DATA MINING& DATA WAREHOUSING COURSE CODE:BCS-403 DEPT OF CSE & IT VSSUT, Burla SYLLABUS: Module – I Data Mining overview, Data Warehouse and OLAP Technology,Data Warehouse Architecture, Stepsfor the Design and Construction of Data Warehouses, A Three-Tier Data WarehouseArchitecture,OLAP,OLAP queries, metadata repository,Data Preprocessing – Data Integration and Transformation, Data Reduction,Data Mining Primitives:What Defines a Data Mining Task? Task-Relevant Data, The Kind of Knowledge to be Mined,KDD Module – II Mining Association Rules in Large Databases, Association Rule Mining, Market BasketAnalysis: Mining A Road Map, The Apriori Algorithm: Finding Frequent Itemsets Using Candidate Generation,Generating Association Rules from Frequent Itemsets, Improving the Efficiently of Apriori,Mining Frequent Itemsets without Candidate Generation, Multilevel Association Rules, Approaches toMining Multilevel Association Rules, Mining Multidimensional Association Rules for Relational Database and Data Warehouses,Multidimensional Association Rules, Mining Quantitative Association Rules, MiningDistance-Based Association Rules, From Association Mining to Correlation Analysis Module – III What is Classification? What Is Prediction? Issues RegardingClassification and Prediction, Classification by Decision Tree Induction, Bayesian Classification, Bayes Theorem, Naïve Bayesian Classification, Classification by Backpropagation, A Multilayer Feed-Forward Neural Network, Defining aNetwork Topology, Classification Based of Concepts from -
Database Vs. Data Warehouse: a Comparative Review
Insights Database vs. Data Warehouse: A Comparative Review By Drew Cardon Professional Services, SVP Health Catalyst A question I often hear out in the field is: I already have a database, so why do I need a data warehouse for healthcare analytics? What is the difference between a database vs. a data warehouse? These questions are fair ones. For years, I’ve worked with databases in healthcare and in other industries, so I’m very familiar with the technical ins and outs of this topic. In this post, I’ll do my best to introduce these technical concepts in a way that everyone can understand. But, before we discuss the difference, could I ask one big favor? This will only take 10 seconds. Could you click below and take a quick poll? I’d like to find out if your organization has a data warehouse, data base(s), or if you don’t know? This would really help me better understand how prevalent data warehouses really are. Click to take our 10 second database vs data warehouse poll Before diving in to the topic, I want to quickly highlight the importance of analytics in healthcare. If you don’t understand the importance of analytics, discussing the distinction between a database and a data warehouse won’t be relevant to you. Here it is in a nutshell. The future of healthcare depends on our ability to use the massive amounts of data now available to drive better quality at a lower cost. If you can’t perform analytics to make sense of your data, you’ll have trouble improving quality and costs, and you won’t succeed in the new healthcare environment. -
A Systematic Review and Taxonomy of Explanations in Decision Support and Recommender Systems
Noname manuscript No. (will be inserted by the editor) A Systematic Review and Taxonomy of Explanations in Decision Support and Recommender Systems Ingrid Nunes · Dietmar Jannach Received: date / Accepted: date Abstract With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems. A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today's increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems con- stantly arise. In this work, we systematically review the literature on expla- nations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advice- giving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when de- signing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objec- tive, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated. Keywords Explanation · Decision Support System · Recommender System · Expert System · Knowledge-based System · Systematic Review · Machine Learning · Trust · Artificial Intelligence I. -
MASTER's THESIS Role of Metadata in the Datawarehousing Environment
2006:24 MASTER'S THESIS Role of Metadata in the Datawarehousing Environment Kranthi Kumar Parankusham Ravinder Reddy Madupu Luleå University of Technology Master Thesis, Continuation Courses Computer and Systems Science Department of Business Administration and Social Sciences Division of Information Systems Sciences 2006:24 - ISSN: 1653-0187 - ISRN: LTU-PB-EX--06/24--SE Preface This study is performed as the part of the master’s programme in computer and system sciences during 2005-06. It has been very useful and valuable experience and we have learned a lot during the study, not only about the topic at hand but also to manage to the work in the specified time. However, this workload would not have been manageable if we had not received help and support from a number of people who we would like to mention. First of all, we would like to thank our professor Svante Edzen for his help and supervision during the writing of thesis. And also we would like to express our gratitude to all the employees who allocated their valuable time to share their professional experience. On a personal level, Kranthi would like to thank all his family for their help and especially for my friends Kiran, Chenna Reddy, and Deepak Kumar. Ravi would like to give the greatest of thanks to his family for always being there when needed, and constantly taking so extremely good care of me….Also, thanks to all my friends for being close to me. Luleå University of Technology, 31 January 2006 Kranthi Kumar Parankusham Ravinder Reddy Madupu Abstract In order for a well functioning data warehouse to succeed many components must work together. -
The Teradata Data Warehouse Appliance Technical Note on Teradata Data Warehouse Appliance Vs
The Teradata Data Warehouse Appliance Technical Note on Teradata Data Warehouse Appliance vs. Oracle Exadata By Krish Krishnan Sixth Sense Advisors Inc. November 2013 Sponsored by: Table of Contents Background ..................................................................3 Loading Data ............................................................3 Data Availability .........................................................3 Data Volumes ............................................................3 Storage Performance .....................................................4 Operational Costs ........................................................4 The Data Warehouse Appliance Selection .....................................5 Under the Hood ..........................................................5 Oracle Exadata. .5 Oracle Exadata Intelligent Storage ........................................6 Hybrid Columnar Compression ...........................................7 Mixed Workload Capability ...............................................8 Management ............................................................9 Scalability ................................................................9 Consolidation ............................................................9 Supportability ...........................................................9 Teradata Data Warehouse Appliance .........................................10 Hardware Improvements ................................................10 Teradata’s Intelligent Memory (TIM) vs. Exdata Flach Cache. -
Enterprise Data Warehouse (EDW) Platform PRIVACY IMPACT ASSESSMENT (PIA)
U.S. Securities and Exchange Commission Enterprise Data Warehouse (EDW) Platform PRIVACY IMPACT ASSESSMENT (PIA) May 31, 2020 Office of Information Technology Privacy Impact Assessment Enterprise Data Warehouse Platform (EDW) General Information 1. Name of Project or System. Enterprise Data Warehouse (EDW) Platform 2. Describe the project and its purpose or function in the SEC’s IT environment. The Enterprise Data Warehouse (EDW) infrastructure will enable the provisioning of data to Commission staff for search and analysis through a virtual data warehouse platform. As part of its Enterprise Data Warehouse initiative, the SEC will begin to integrate data from various systems to provide more comprehensive management and financial reporting on a regular basis and facilitate better decision-making. (SEC Strategic Plan, 2014-2018, pg.31) The EDW will replicate source system-provided data from other operational SEC systems and provide a simplified way of producing Agency reports. The EDW is designed for summarization of information, retrieval of information, reporting speed, and ease of use (i.e. creating reports). It will enhance business intelligence, augment data quality and consistency, and generate a high return on investment by allowing users to quickly search and access critical data from a single location and obtain historical intelligence to analyze different time periods and performance trends in order to make future predictions. (SEC FY2013 Agency Financial Report, pg.31-32) The data content in the Enterprise Data Warehouse platform will be provisioned incrementally over time via a series of distinct development projects, each of which will target specific data sets based on requirements provided by SEC business stakeholders. -
High-Performance Analytics for Today's Business
Teradata Database: High-Performance Analytics for Today’s Business DATA WAREHOUSING Because Performance Counts Already the foundation of the world’s most successful data warehouses, Teradata Database is designed for decision support and parallel implementation. And it’s as versatile The success of your data warehouse has always rested as it is powerful. It supports hundreds of installations, on the performance of your database engine. But as from three terabytes to huge warehouses with petabytes your analytics and data requirements grow increasingly (PB) of data and thousands of users. Teradata Database’s complex, performance becomes more vital than ever. inherent parallelism and architecture provide more So does finding a faster, simpler way to manage your data than pure performance. It also offers low total cost of warehouse. You can find that combination of unmatched ownership (TCO) and a simple, scalable, and self-managing performance, flexibility, and efficient management in the solution for building a successful data warehouse. Teradata® Database. With the Teradata Software-Defined Warehouse, In fact, we continually enhance the Teradata Database customers have the flexibility to segregate data and to provide you and your business with new features users on a single system. By combining Teradata and functions for even higher levels of flexibility and Database’s secure zones with Teradata’s industry-leading performance. Work with dynamic data and schema-on- mixed workload management capabilities, multi-tenant read operational models together with structured data economies and flexible privacy regulation compliance through completely integrated JSON and Avro data. become possible in a single Teradata Database system. Query processing performance is also accelerated by the world’s most advanced hybrid row/column table storage capability and in-memory optimization and vectorization for in-memory database speed.