Data Warehousing with Informix: Best Practices

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Summary: Haven't read yet. Reviewed desc at Prentice Hall
Review: KEY BENEFIT:This is the first book that brings together the background, tips and specific techniques INFORMIX database users need to successfully deploy and manage a data warehouse. KEY TOPICS:In the first section of this book, noted data warehousing expert Vidette Poe introduces the core issues surrounding today's data warehouse, covering construction, administration and template data models. Next, Bill Inmon -- "the father of data warehousing" -- covers the latest data warehousing technologies, including 64-bit computing, scalable data mining and INFORMIX-Universal Server. Then, leading technologists cover the deployment of INFORMIX-based data warehouses in depth. Topics include: INFORMIX-Online Extended Parallel Server; proliferating data warehouses beyond power users; building data warehouses with INFORMIX relational databases; and high-availability transaction processing environments based on INFORMIX.MARKET:For all data warehouse and database administrators and decision makers using or considering Informix technology. For more information, see the book preface •Covers every aspect of data warehousing, with special emphasis on INFORMIX issues and technology -- especially INFORMIX-Universal Server. •Scalability, parallel architectures, data mining, high-availability transaction processing, and more. •Includes major contributions from data warehousing pioneer Bill Inmon and decision support expert Vidette Poe! Table Of ContentsPart I. The Decision Support Community . 1. The DSS Community: Tourists, Explorers, and Farmers by William H. Inmon Introduction. The DSS Community. Tourists. Explorers. Farmers. Development Life Cycles. Database Design. Different Data Warehouses. Infrastructure. Tools. The Cycle of DSS Users. The Organization Chart. Summary. About the Author. About Pine Cone Systems. 2. Managing the Data Warehouse: The Advent of the Data Warehouse Administrator by William H. Inmon. Managing the Data Warehouse Environment. Oper!ational and Informational Differences. The Role of the DWA. Managing Volumes of Data. Keeping Costs Down. Creating and Managing the Technical Infrastructure. Fostering an Organizational Environment. Creating a Proper Architecture. Summary. About the Author. About Pine Cone Systems. Part II. Design and Implementation for Decision Support. 3. Using a Rational Approach to Build Your Data Warehouse by Dale Mietla and Marvin Miller. Introduction. Key Components. Data Warehouse Products. Data Warehouse Services. Data Warehousing Process. What Is a Process? Project Startup . Scope and Purpose Confirmation. Knowledge Analysis. Knowledge Requirements Definition. Data Source and Quality Analysis. Knowledge Use Analysis. Solution Design. Solution Architecture Design. Testing Strategy and Design. Procedure Design . Data Warehouse Environment Installation. Solution Implementation.Warehouse Solution Development. Warehouse Loading and Testing. Data Warehouse Solution Deployment. Project Wrap-Up. Warehouse Solution Review. Iterative Application of the Process. Concurrent Engineering Shortens Cycle Time. Keys to Project Success. Conclusion . About the Authors. About NewTHINK, Inc. About Digital Equipment Corporation. 4. Starting the Data Warehouse from a Data Model by Larry Heinrich. What Are Template Data Models? Why Use Template Data Models? The Cost of Entry. What Types of Industries Are Supported? Everything Begins with the Enterprise Data Model. Business Areas Are the Building Blocks for the Data Warehouse. Using Template Data Models to Build the Data Warehouse and Data Marts. Comparing the Costs. Guidelines for Using Template Data Models. The Right Tools Make the Job Easier. Summary. About the Author. About Allied Data Resource Management. 5. Integrating Data to Populate the Data Warehouse by Patricia Klauer and Vidette Poe. Introduction. Understanding the Business Purpose of the Data Warehouse. Defining Data versus Information. The Data Integration Process. Data Architecture. Dat!a Architecture Tip. Metadata Data Integration Phases. Data Sourcing. Data Consolidation Steps. Analyze Source Data Documentation. Source Data Documentation Tip. Flatten Out the Data Into Logical Records. Logical Record Tip. Perform Domain Analysis. Representative Data Tip. Determine the Primary Keys. Data Analyst Skill Set Tip. Identify Foreign Keys. Synonyming Tip. Data Analysis Needed for Data Consolidation. Identifying Overlapping Data: Subject Area Analysis. Identifying Overlapping Data: Synonyms, Homonyms, and Analogs. Analyzing Data to Integrate It into an Existing Data Warehouse. Data Analysis Tip. Understanding Business Rules and Nuances of Meaning. Business Rule Tip. Data-Driver Analysis. Data-Driven Analysis Tip. Data Conversion. Map Source File Attributes to the Data Warehouse's Physical Data Structure. Map Source Attributes' Allowable.Values to Target Values. Specify Default Values. Conversion Specifications. Data Population. Write Conversion Programs. Test the Conversion. Determine Exception Processing. Collect Statistics. Conduct Quality Assurance. Perform Stress Test . Summary. About the Authors. About Manage Data, Inc. About InER-G Solutions, Ltd. @chapter 6 = Designing an OLAP Data Mart on Relational Databases by Jonathan Kraft. Introduction. Classic Entity-Relationship Modeling and Decision Support. Proprietary Multidimensional Databases. Dimensional Modeling. Dimension Elements. Dimension Attributes. The Star Schema. Denormalization of Dimensions. Advantages of Star Schema in Dimensional Modeling. Aggregation. Aggregating the Multidimensional Data Warehouse. How Much to Aggregate? Choosing the Right Aggregates. Sample Aggregation Sizing. Simulation Procedures. Simulation Results. Incremental versus Full Aggregation. E xtending Dimensional Modeling. Normalizing the Dimensions . The Snowflake Schema. Disadvantages of Normalization. Partial Normalization. Conclusion. About the Author. 7. A Data Mining Tutorial by Alice Landy. Introduction. Scalable Data Mining and Knowledge Discovery.! Scalability in Data Mining. Parallel Processing. Out of Core Processing. Working with Databases. Using All the Data . Knowledge Discovery and Deployment. Using Data Mining Software. Define the Business Problem. Costs and Benefits. The Time/Perfection Tradeoff. Locate the Data. Organize the Data. Dimensions . Prepare the Data. Cleaning Data. Organizing Data. Create a Historical Data Set. Create a Model. Data Mining Models. Multiple Learning Tools. Build Models. Pruning a Tree and Testing the Subtrees. Evaluating a Subtree. Predicting the Response Variable. Optimizing Trees. Neural Networks. Creating a Neural Network. Training the Network. Network Weights. Training Algorithms. Optimizing Neural Networks. Match Models. Optimizing Match Models. Use the Model. Analyze the Results. Margin and ROI. Update Your Model. Measuring Success. About the Author. About Thinking Machines Corporation. 8. Sampling: The Latest Breakthrough in Decision-Support Technology by Jonathan Kraft. Introduction: Increasing the Demand for Information. Business Trend Discovery. Drilling to Detail. Sampling: A Usability and Scalability Breakthrough. Aggregation and Its Uses. Configuration 1: Exhausive Aggregation. Configuration 2: Sparse Aggregation. Sampling: A Scalability and Maintenance Windfall . Accuracy and Confidence: A Complex Paradigm Made Simple. Trusting Samples. Conclusion. About the Author . Part III. Hardware, storage, and Backup Issues in a Data Warehouse. Chapter 9 Optimal Architecture for Enterprise-Class Data Warehousing by Steve Deck 167 The Evolution to High-End Data Warehouses 167 Data Warehouse Overview 168 Definition 168 Planning and Implementation 169 Support and Management 169 Clustered SMP versus MPP in Data Warehousing 170 Massively Parallel Processors 170 Clustered Symmetric Multiprocessors 171 Informix and HP in Enterprise Data Warehousing 172 Complementary Architectures 173 Informix Dynamic Server with the Extended Parallel Option and Advanced Decision Support Option 173 DSA: Parallel Origin 174 HP 90!00 Enterprise Parallel Server 176 HP-UX Operating System/PA-RISC Architecture 176 Hewlett-Packard Enterprise Parallel Server 176 Informix Dynamic Server with the Extended Parallel Option and Advanced Decision Support Option, and the HP EPS Clustered SMP Environment 178 Scalable RDBMS 178 Open Systems Hardware 179 Real-World Performance 179 Data Partitioning 181 Control Partitioning 181 Query Execution 182 Intraserver Communication 182 High Availability 183 Dual-Ported Disks and Other Disk Solutions 183 Early Benchmark Results 186 Conclusion 187 About the Author 188 Chapter 10 Data Warehousing and the Value of 64-Bit Computing by Marvin Miller 189 Introduction 189 Business Work Flow 190 Work Flow for Operational Data Systems (OLTP) 190 Work Flow for OLTP and DSS 191 Data Warehousing's Hidden Relationship 192 Examples 192 Market-Basket Analysis 192 Buying Patterns 192 The Relation of Samples to Prescriptions 192 The Value of Data Relationships 193 Data Warehouse Examples 193 Linking the ODS and DSS 194 Accessing the Operational Data Store 195 W

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