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The life cycle approach provides a roadmap for the development of data warehouses, and the overall structure and steps of the life cycle approach are
Define Business requirements
Technical path
Technical Architecture Design
Selection and installation of products
Data path
Dimension modeling
Physical design
ETL Des
Objective:Ready to systematize a set of distributed Data Warehouse Modeling Practice Guide, the first list of the table, is to design a goal for themselves.The first part of the basic articleChapter One concept and definition of data Warehouse1.1 Data Management System1.2 Data
has implemented Probable Cost of Ownership for Data Warehouses over five years? (PCO) lower than Oracle and NCR Teradata.
Foreseeable scalability and unlimited scalability are the key criteria of the Business Intelligence platform. DB2 meets this requirement through its unique shared-nothing architecture. Scalability is applicable to both large and small databases.
Scalability and price are important, but
question of answering enterprise.In addition, the daily operation of the enterprise information system is often composed of a number of traditional systems, incompatible data sources, databases and applications together constitute a complex collection of data, the various parts can not communicate with each other. From this point of view: the current implementation of the application system is the user spe
sources, and databases and applications. Each part cannot communicate with each other. From this perspective: the application system currently executed is an irreplaceable system built by users, especially system data. The purpose of establishing a data warehouse is to integrate and organize the data from different so
This paper is compiled from Oracle documentation
Warehouse Builder 11g Architecture and components
Oracle Warehouse Builder is an information integration tool that can transform data into high-quality information using an Oracle database. The Oracle database is the central component of the
Tags: ash t-SQL query _id important SSD round mode for copy tableWindows Azure Platform Family of articles Catalog In the previous article, I introduced the basic content of the MPP architectureIn this chapter, I introduce you to the architecture of Azure SQL Data Warehouse (SQL DW). The 1.SQL DW is divided into head node and work node, denoted by control node
Development and implementation of the business intelligence platform for Small and Medium Enterprises (data warehouse, Bi system, and real project practices)Course category: data and data warehouseSuitable audience: elementaryLesson count: 76 lessonsTechnologies used: Data
data sources, and databases and applications. Each part cannot communicate with each other. From this perspective: the application system currently executed is an irreplaceable system built by users, especially system data. The purpose of establishing a data warehouse is to integrate and organize the
multiple traditional systems, incompatible data sources, and databases and applications. Each part cannot communicate with each other. From this perspective: the application system currently executed is an irreplaceable system built by users, especially system data. The purpose of establishing a data warehouse is to i
package diagram can be expressed in a similar way.(4) This step can be used to construct a star chart based on the information packaging graph, as shown in 3-26. Then, based on the actual situation, connect the detailed category object to the Star Graph to obtain the snowflake model of the enterprise data warehouse. For example, in the AdventureWorks Business Database, products have been hierarchically cla
data to the CDCEtl considerationsETL considerationsThoughts on data dimension-- Theoretical DiscussionData warehouse-ODS ConceptBusiness Intelligence and data warehouse seminar-Chinese documentsAbout data
In the field of data warehousing, we often hear some personal or organizational debate about the theory of data warehousing, whether it belongs to Bill Inmon or Ralph Kimball faction. Below we will describe the difference between the two.
Bill Inmon Paradigm:The Data Warehouse
team focus on the results of the project requirements discussion rather than the process itself.
Since your communication with your client is to understand what type of data is stored and how to store it efficiently, you may need to use a new approach (along with your users) to observe the data rather than directly process the data. You can try to find hidden i
data, you may need to (with your users) adopt a new method to observe the data, instead of directly processing data. You can try to find hidden information, such as digital fluctuations in a period of time. Instead of trying to find the answer to the project's needs, you need to find the answer.
4. Lead by technology/information librarySince the implementation
users) adopt a new method to observe the data, instead of directly processing data. You can try to find hidden information, such as digital fluctuations in a period of time. Instead of trying to find the answer to the project's needs, you need to find the answer.
4. Lead by technology/information library
Since the implementation of the data
1. Warehouse Style
In the repository style. There are two different components: the central data structure shows the current state. The independent component is executed on the central data storage. The interaction between the warehouse and the external component is greatly changed in the system.
If a process execution
indicators that need to be identified in this step include:L ability to manage large data volumes;• Ability to perform flexible data access;• Ability to reorganize data based on data models;L Transparent data transmission and reception capability;• Ability to periodically l
: The time base change is difficult to grasp. The existing operational data is usually the current value, precision controllable, can be updated, but the data in the data warehouse can not be updated, so these data must be accompanied by time elements, when the actual operat
Label:There are two kinds of database design models widely used in Data warehouse: Relational type and multidimensional type. It is generally believed that the relational model in the design method of Data Warehouse is "Inmon" method and multidimensional model is "Kimball" m
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