Creating a Data Warehouse Using SQL Server
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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 -
Operational Database Offload
Operational Database Offload Partner Brief Facing increased data growth and cost pressures, scale‐out technology has become very popular as more businesses become frustrated with their costly “Our partnership with Hortonworks is able to scale‐up RDBMSs. With Hadoop emerging as the de facto scale‐out file system, a deliver to our clients 5‐10x faster performance Hadoop RDBMS is a natural choice to replace traditional relational databases and over 75% reduction in TCO over traditional scale‐up databases. With Splice like Oracle and IBM DB2, which struggle with cost or scaling issues. Machine’s SQL‐based transactional processing Designed to meet the needs of real‐time, data‐driven businesses, Splice engine, our clients are able to migrate their legacy database applications without Machine is the only Hadoop RDBMS. Splice Machine offers an ANSI‐SQL application rewrites” database with support for ACID transactions on the distributed computing Monte Zweben infrastructure of Hadoop. Like Oracle and MySQL, it is an operational database Chief Executive Office that can handle operational (OLTP) or analytical (OLAP) workloads, while scaling Splice Machine out cost‐effectively from terabytes to petabytes on inexpensive commodity servers. Splice Machine, a technology partner with Hortonworks, chose HBase and Hadoop as its scale‐out architecture because of their proven auto‐sharding, replication, and failover technology. This partnership now allows businesses the best of all worlds: a standard SQL database, the proven scale‐out of Hadoop, and the ability to leverage current staff, operations, and applications without specialized hardware or significant application modifications. What Business Challenges are Solved? Leverage Existing SQL Tools Cost Effective Scaling Real‐Time Updates Leveraging the proven SQL processing of Splice Machine leverages the proven Splice Machine provides full ACID Apache Derby, Splice Machine is a true ANSI auto‐sharding of HBase to scale with transactions across rows and tables by using SQL database on Hadoop. -
Product 360: Retail and Consumer Industries
PRODUCT 360: RETAIL AND CONSUMER INDUSTRIES MARKLOGIC WHITE PAPER • NOVEMBER 2015 PRODUCT INFORMATION IS COMPLEX A major challenge for Retail and Consumer companies today is product proliferation and product complexity. An electronics retailer for example may have over 70,000 products in its catalog, while it is not uncommon for an industrial distributor to have over a million products and represent over 1,000 suppliers. Products HD typically have short shelf lives. In electronics for example it’s not uncommon for a new model to be released every year. And, a “Product” is not just a physical SKU (stock keeping unit) but a complex combination of structured HD and unstructured data that helps consumers search for, evaluate, compare, and choose their desired purchase. Product information includes a variety of data elements Product information includes a wide variety of data elements which are generated and stored in multiple locations, for example: WHY IS “PRODUCT 360” • Product descriptive information (e.g. size, color, IMPORTANT? material, nutritional information, usage, and other Creating, maintaining, and managing a 360 degree view elements that define it) of products is at the core of competitive differentiation • Digital images and videos – and in fact even survival – for retail and consumer • Customer ratings and reviews companies. • Dynamic pricing and promotions • Availability in-stock Key benefits of a Product 360 include: • Consumer loyalty information (who’s most likely to buy it) REVENUE GROWTH • Related products and accessories Today just 3% of on-line e-commerce transactions actually result in a sale. E-Commerce is the fastest These data elements often sit in different databases and growing channel for retailers, and sales via e-commerce legacy systems, making accessing them a challenge. -
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. -
Operational Database Overview Date Published: 2020-02-29 Date Modified: 2021-02-04
Cloudera Runtime 7.2.7 Operational Database Overview Date published: 2020-02-29 Date modified: 2021-02-04 https://docs.cloudera.com/ Legal Notice © Cloudera Inc. 2021. All rights reserved. The documentation is and contains Cloudera proprietary information protected by copyright and other intellectual property rights. No license under copyright or any other intellectual property right is granted herein. Copyright information for Cloudera software may be found within the documentation accompanying each component in a particular release. Cloudera software includes software from various open source or other third party projects, and may be released under the Apache Software License 2.0 (“ASLv2”), the Affero General Public License version 3 (AGPLv3), or other license terms. Other software included may be released under the terms of alternative open source licenses. Please review the license and notice files accompanying the software for additional licensing information. Please visit the Cloudera software product page for more information on Cloudera software. For more information on Cloudera support services, please visit either the Support or Sales page. Feel free to contact us directly to discuss your specific needs. Cloudera reserves the right to change any products at any time, and without notice. Cloudera assumes no responsibility nor liability arising from the use of products, except as expressly agreed to in writing by Cloudera. Cloudera, Cloudera Altus, HUE, Impala, Cloudera Impala, and other Cloudera marks are registered or unregistered trademarks in the United States and other countries. All other trademarks are the property of their respective owners. Disclaimer: EXCEPT AS EXPRESSLY PROVIDED IN A WRITTEN AGREEMENT WITH CLOUDERA, CLOUDERA DOES NOT MAKE NOR GIVE ANY REPRESENTATION, WARRANTY, NOR COVENANT OF ANY KIND, WHETHER EXPRESS OR IMPLIED, IN CONNECTION WITH CLOUDERA TECHNOLOGY OR RELATED SUPPORT PROVIDED IN CONNECTION THEREWITH. -
P6 Reporting Database Planning and Sizing
P6 Reporting Database Ver 3.0 Planning and Sizing An Oracle White Paper December 2011 Disclaimer The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. 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 for Oracle’s products remains at the sole discretion of Oracle. Contents Introduction ...................................................................................................................................... 5 Critical Performance Factors ............................................................................................................ 5 Four Key Areas of the ETL ................................................................................................................. 6 Pulling Data between Servers ..................................................................................................... 6 Merging Updates into Target Database ...................................................................................... 6 PL/SQL-based Transformations .................................................................................................. 7 Planning Process .............................................................................................................................. 7 Why Planning is Key ......................................................................................................................... -
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. -
What Is Database? Types and Examples
What is database? Types and Examples Visit our site for more information: www.examplanning.com Facebook Page: https://www.facebook.com/examplanning10/ Twitter: https://twitter.com/examplanning10 TABLE OF CONTENTS Sr. Description 1 What is database? 2 Different definitions of database 3 Growth of Database 4 Elements of Database 5 Components of database 6 Database System Environment 7 Types of Databas 8 Characteristics of database 9 Advantages of Database 10 Disadvantages of Database What is Database? A database is a collection of information or data which are organized in such a way that it can be easily accessed, managed and retrieved. Database is abbreviated ad DB. Different definitions of database. “a usually large collection of data organized especially for rapid search and retrieval (as by a computer) an online database” (merriam-webster) “a comprehensive collection of related data organized for convenient access, generally in a computer.” (dictionary) A database is an organized collection of data. (Wikipedia) What is data? It is used as both singular and plural form. It can be a quantity, symbol or character on which operations are performed. Data is information which are converted into digital form. Growth of Database Database was evolved in 1960's started with the hierarchical database. Relational database was invented by EF Codd in 1970s while object oriented database was invented in 1980s. In 1990s object oriented database rose with the growth of object oriented programming languages. Nowadays, databases with SQL and NoSQL are popular. Elements of Database Database elements are fields, rows, columns, tables. All these are building blocks of database. -
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 Software Market: Billy Fitzsimmons +1 312 364 5112
Equity Research Technology, Media, & Communications | Enterprise and Cloud Infrastructure March 22, 2019 Industry Report Jason Ader +1 617 235 7519 [email protected] Database Software Market: Billy Fitzsimmons +1 312 364 5112 The Long-Awaited Shake-up [email protected] Naji +1 212 245 6508 [email protected] Please refer to important disclosures on pages 70 and 71. Analyst certification is on page 70. William Blair or an affiliate does and seeks to do business with companies covered in its research reports. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. This report is not intended to provide personal investment advice. The opinions and recommendations here- in do not take into account individual client circumstances, objectives, or needs and are not intended as recommen- dations of particular securities, financial instruments, or strategies to particular clients. The recipient of this report must make its own independent decisions regarding any securities or financial instruments mentioned herein. William Blair Contents Key Findings ......................................................................................................................3 Introduction .......................................................................................................................5 Database Market History ...................................................................................................7 Market Definitions -
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. -
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.