Building The Data Warehouse - Chapter 01 PDF
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2022
Song Nguyen
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Summary
This document is a chapter from a broader work on building data warehouses. It covers the evolution of decision support systems and discusses various aspects of data warehousing, including the advent of DASD, PC/4GL technology, extract programs, and the Spider Web. It also explores problems with naturally evolving architectures like lack of data credibility and productivity, the transition from data to information, and how data is integrated into the system. The document analyzes the system development life cycle for data warehouses and looks at patterns of hardware utilization and the setting for re-engineering.
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Building Data WareHouse by Inmon Chapter 1: Evolution of Decision Support System Prepared By: Song Nguyen Date: 05/09/2022 1.1 The Evolution The need to synchronize Sections data upon update The advent of DASD The complexity of...
Building Data WareHouse by Inmon Chapter 1: Evolution of Decision Support System Prepared By: Song Nguyen Date: 05/09/2022 1.1 The Evolution The need to synchronize Sections data upon update The advent of DASD The complexity of PC/4GL Technology maintaining programs Enter the Extract Program The Spider Web The complexity of developing new programs The need for extensive amounts of hardware to support all the master files 1.1.1 The Advent of DASD 1970: Direct Access Storage DBMS: Data base Management systems Mid-1970s OLTP: Online Transaction Processing Goals: Faster access Ease of Management 1.1.2 PC/4GL Technology 1980 PC and 4th Generation Language MIS: Management Information System DSS: Decision Support System – Single database 1.1.3 Enter the Extract Program 1.1.4 The Spider Web 1.2 Problems with the Naturally Evolving Architect Lack of Data Credibility Problems with Productivity From data to Information A Change in Approach The Architected Environment Data Integration in the Architected Envinronment Who is the User 1.2.1 Lack of Data Credibility 1.2.1 Lack of Data Credibility (cont) Natural evolving architecture challenges Data Credibility Productivity Inability to transform data to information Lack of Data Creditbility No time basis of data The Algorithmic differential of data The Levels of Extraction The problem of the external data No common source of data from the beginning 1.2.2 Problems with Productivity Many files and collections 🡪 how to create correct report ? Locate and analyze the data for report Compile the data for the report Get Programmer/analyst resources to accomplish these two tasks. Complications Lots of programs have been written Each Program must be customized The program cross every technology that the company uses 1.2.2 Problems with Productivity (c) 1.2.2 Problems with Productivity (c) 1.2.3 From Data to Information 1.2.4 A Change in Approach 1.2.4 A Change In Approach (con’t) 1.2.5 The Architect Environment 1.2.5.1 A simple Example-A Customer 1.2.6 Data Integration in the Architected Environment 1.2.7 Who Is the Users ? The attitude of the DSS analyst is important for the following reasons: 1. It is legitimate. This is simply how DSS analysts think and how they conduct their business. 2. It is pervasive. DSS analysts around the world think like this. 3. It has a profound effect on the way the data warehouse is developed and on how systems using the data warehouse are developed. The classical system development life cycle (SDLC) does not work in the world of the DSS analyst 1.3 The Development Life Cycle 1.4 Patterns of Hardware Utilization 1.5 Setting the Stage for Re-engineering 1.5 Setting the Stage for Re-engineering-c 1.6 Monitoring the Data Warehouse env. Identifying what growth is occurring, where the growth is occurring, and at what rate the growth is occurring Identifying what data is being used Calculating what response time the end user is getting Determining who is actually using the data warehouse Specifying how much of the data warehouse end users are using Pinpointing when the data warehouse is being used Recognizing how much of the data warehouse is being used Examining the level of usage of the data warehouse 1.6 Monitoring the Data Warehouse environment con’t The data profiles that can be The need to monitor activity in created during the the data warehouse is data-monitoring process include illustrated by the following the following: questions: 1. What data is being accessed? 1. A catalog of all tables in the 2. When? warehouse 3. By whom? 2. A profile of the contents of 4. How frequently? those tables 5. At what level of detail? 3. A profile of the growth of the 6. What is the response time for tables in the data warehouse the request? 4. A catalog of the indexes 7. At what point in the day is the available for entry to the tables request submitted? 5. A catalog of the summary 8. How big was the request? tables and the sources for the 9. Was the request terminated, or summary did it end naturally? Summary Origin of data warehouse Architecture that fits data warehouse Evolution of information processing Found in Operational environment ends up in the integrated warehouse System Development Life Cycle paradigm shifts Decision Support System … Who are the users ?
