Building and Maintaining a Data Warehouse
Auteur : Silvers Fon
As it is with building a house, most of the work necessary to build a data warehouse is neither visible nor obvious when looking at the completed product. While it may be easy to plan for a data warehouse that incorporates all the right concepts, taking the steps needed to create a warehouse that is as functional and user-friendly as it is theoretically sound, is not especially easy. That?s the challenge that Building and Maintaininga Data Warehouse answers.
Based on a foundation of industry-accepted principles, this work provides an easy-to-follow approach that is cohesive and holistic. By offering the perspective of a successful data warehouse, as well as that of a failed one, this workdetails those factors that must be accomplished and those that are best avoided.
Organized to logically progress from more general to specific information, this valuable guide:
- Presents areas of a data warehouse individually and in sequence, showing how each piece becomes a working part of the whole
- Examines the concepts and principles that are at the foundation of every successful data warehouse
- Explains how to recognize and attend to problematic gaps in an established data warehouse
- Provides the big picture perspective that planners and executives require
Those considering the planning and creation of a data warehouse, as well as those who?ve already built one will profit greatly from the insights garnered by the author during his years of creating and gathering information on state-of-the-art data warehouses that are accessible, convenient, and reliable.
Date de parution : 09-2019
15.6x23.4 cm
Date de parution : 04-2008
Ouvrage de 320 p.
15.6x23.4 cm
Thèmes de Building and Maintaining a Data Warehouse :
Mots-clés :
Data Warehouse; Da Ta; Data; Data Warehouse Customers; Ta Ba; Data Warehouse Teams; Source System; Bi Report; Data Warehouse Designer; ETL Process; Metadata Repository; RDBMS Vendor; Real Time Data Warehousing; Dynamic Metadata; Bi Analyst; Qu Ali; Op Era; Data Warehouse Processes; Data Flow Diagram; OLAP Application; Static Metadata; Data Quality Measurements; Candidate Row; Data Mining Tool; Em PL