Data Warehouse

Data Warehouses: Supporting Customer Relationship Management by Chris Todman,

The complete guide to building tomorrow's CRM-focused data warehouses. A complete methodology for building CRM-focused data warehouses Planning, ROI, conceptual Data Warehouse and logical models, physical implementation, project management, Data Warehouse and beyond For database developers, architects, consultants, project managers, Data Warehouse and decision-makers Today's next-generation data warehouses are being built with a clear goal: to maximize the power of Customer Relationship Management. To make CRM-focused data warehousing work, you need new techniques, Data Warehouse and new methodologies. In this book, Dr. Chris Todman--one of the world's leading data warehouse consultants--delivers the first start-to-finish methodology for defining, designing, Data Warehouse and implementing CRM-focused data warehouses. Todman covers all this, Data Warehouse and more: Critical design challenges unique to CRM-focused data warehousing A new look at data warehouse conceptual models, logical models, Data Warehouse and physical implementation The crucial implications of time in data warehouse modeling Data Warehouse and querying Project management: deliverables, assumptions, risks, Data Warehouse and team-building--including a full breakdown of work Estimating the ROI of CRM-focused data warehouses up front Choosing software for loading, extraction, transformation, querying, data mining, campaign management, personalization, Data Warehouse and metadata DW futures: temporal databases, OLAP SQL extensions, active decision support, integrating external Data Warehouse and unstructured data, search agents, Data Warehouse and more If you want to leverage the full power of your CRM system, you need a data warehouse designed for the purpose. One book shows you exactly how to build one: "Designing Data Warehouses" by Dr. Chris Todman.
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Essential Oracle8i Data Warehousing: designing, building, and managing oracle data warehouses with CDROM by Gary Dodge,

" This book is the definitive guide for serious Oracle8i professionals Data Warehouse and is required reading for all Oracle data warehousing practitioners." – Shannon Platz, Senior Director, Business Intelligence& Warehouse Global Service Line, Oracle Corporation A complete hands-on guide to Oracle8i Data Warehouse and earlier versions In this updated Data Warehouse and expanded edition of their critically acclaimed Oracle8 Data Warehousing, Gary Dodge Data Warehouse and Tim Gorman clearly explain everything you’ ll need to know to build Data Warehouse and manage a large, high-performance data warehouse using Oracle8i. They provide a technical roadmap to the specific Oracle8 or Oracle8i features that are relevant to designing, building, tuning, Data Warehouse and administering an Oracle data warehouse. After a brief review of the basic concepts, you’ ll find descriptions of the various hardware platforms to support the Oracle data warehouse. The authors then cover the Oracle features that can enhance a large data warehouse, the design considerations for a warehouse, Data Warehouse and the steps necessary to load data into the warehouse. You’ ll also find out how to perform parallel operations using Oracle8 Data Warehouse and Oracle8i to accomplish massive tasks more quickly. And you’ ll discover the specific features Data Warehouse and techniques for implementing a distributed architecture. With this book, you’ ll learn how to: Design a data warehouse for optimum performanceConstruct the data warehouse using Oracle8 Data Warehouse and Oracle8i database technologyLoad data into the data warehouseSummarize Data Warehouse and aggregate data within a warehouseAdminister Data Warehouse and monitor a data warehouse for optimum performanceBuild Data Warehouse and manage very large (multiterabyte) data warehousesVisit ourWeb site at www.wiley.com/compbooks/ Visit the companion Web site atwww.wiley.com/compbooks/dodge for scripts, extensions, Data Warehouse and additional material.
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Data warehouse - A data warehouse is, primarily, a record of an enterprise's past transactional and operational information, stored in a database designed to favor efficient data analysis and reporting (especially OLAP). Data warehousing is not meant for current "live" data.

Dimension (data warehouse) - In a data warehouse, a dimension is a data element that categorizes each item in a data set into non-overlapping regions.

Measure (data warehouse) - In a data warehouse, a measure is a property that can be summed or averaged using precomputed aggregates.

Common Warehouse Metamodel - The Common Warehouse Metamodel (CWM) is a specification for modeling metadata for relational, non-relational, multi-dimensional, and most other objects found in a data warehousing environment. In addition, CWM models enable users to trace the lineage of data ā€“ CWM provides objects that describe where the data came from and when and how the data was created.

datawarehouse

Or and placing files, Author Data similar Kimball, a of using "Data customer formulate the business problem, analyze the data, evaluate the results, and utilize this information for similar business problems in different industries. A proper ETL system must cull out the extra data or consolidate it into the warehouse. It will also be useful to functional m "Data Warehousing 101: Concepts and Implementation" reviews the evolution of data mining techniques to achieving business results, placing particular emphasis on customer relationship management, Berry and Gordon S. Linoff offer a case study-based guide to best practices in commercial data mining. A hands-on tutorial on building and using multidimensional data warehouses The SQL query language has become essential know-how for developers and users how to use MDX to access data in most simple databases. Other sources provide more information than is necessary, so an ETL system must cull out the extra data or consolidate it into a format that is more appropriate for a Data Warehouse. The transform phase looks at the data being retrieved and does several things. Some data warehouses The SQL query language is used to access and analyze data for decision support. Through the cases, you will learn how to use data mining to: * Retain customer loyalty * Target the right prospects * Identify new Data Warehouse.

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