degenerate dimension in data warehouse

Label:In short, the database is a transaction-oriented design, the Data Warehouse is a theme-oriented design.Databases generally store online transaction data, and data warehouses typically store historical data.Database design is to avoid redundancy as far as possible, generally adopt the rule of conforming to the pat

/DB2 OLAP Server supports the definition of "dimension" and data loading. ESSBASE/DB2 OLAP server is not a ROLAP (relational OLAP) server, but rather a hybrid HOLAP server (ROLAP and MOLAP) that is stored in the system-specified DB2 after the essbase completes the data load UDB database. Strictly speaking, IBM itself does not provide a complete

to design; Data Warehouse design is intentionally introduced redundancy, the use of anti-paradigm approach.4, the provision of different functions: The database is designed to capture data, the Data Warehouse is designed for the analysis of

: Different starting points: databases are designed for transactions and data warehouses are designed for topics. Different Data Storage: databases generally store online transaction data, while data warehouses generally store historical data. Different design rules:

adapterStorage Management Software Appendix C: Server ArchitectureAppendix D: EMC CLARiiON StorageTopology Appendix E: storage isolationConfigure your storage Appendix F: script Partition a relational data warehouse The following sections briefly explain the concept of a relational data warehouse, the benefits of part

differences are as follows:(1) database is a transaction-oriented design, the Data Warehouse is subject-oriented design.(2) database is generally stored online transaction data, Data Warehouse storage is generally historical data

Building a real data warehouse in a data warehouse may be a huge project. There are many different devices, methods, and theories. What is the greatest common value? What are facts and what are theme related to these facts? And how do you mix, match, merge, and integrate systems that may have existed for decades with s

Data Data Warehouse Learning Experience A Concept 1. Data Warehouse: Refers to the theme-oriented, consistent, different time, stable data collection, to support the management of the decision support process. In a broad sense,

region in the past five years? In this case, you can use the multi-dimensional cluster MDC feature provided by DB2 You can obtain rows with the same dimension value in batches. 2.2.4 use the materialized query table (MQT) function. MQT is a result set defined by a query statement, so that you do not need to dynamically build this result set during query execution. MQT has three advantages: DB2 can automatically rewrite the query to use MQT. The resul

This course aims to achieveIn the SQL Server Enterprise ManagerData conversion Service (DTS) designerCreate an analysis services processing taskTo achieve automatic extraction, conversion, and filling of the data required by the Data Warehouse-------------------------------(For details, refer toCreate an analysis services processing task)This example is as follow

Compare | Data IBM, Oracle, Sybase, CA, NCR, Informix, Microsoft, SAS and other powerful companies have launched their own data warehousing solutions (through acquisitions or research and development). Professional software companies such as Bo and Brio also have a place in the front-end online analytical processing tools market. The following is an analysis and comparison of the performance and characteris

Microsoft's Parallel Data Warehouse (Parallel data Warehouse, abbreviated PDW) was released last year with SQL Server 2008 R2, designed to compete with Oracle Exadata and Teradata. PDW a true sense of the ability to mix workloads, users can extend data from multiple physical

of the database table data into the Data Warehouse). This approach is suitable for reference types of source data, such as postal codes. Reference source data is typically the source of a dimension table. If the amount of source

A brief explanation of common nouns in data data Warehouse Data Warehouse in the middle of the 80 's, Mr. William H.inmon, the "Father of the warehouse", defined the concept of data war

Partitioning a relational data warehouse The following sections will briefly explain the concept of a relational data warehouse, the benefits of partitioning a relational data warehouse, and the benefits of migrating to Microsoft

Low 0.01 3000.00 Grid MED 3000.01 6000.00 Grid High 6000.01 99999999.99 Each fragment has a start value and an end value. The granularity of the segment is the gap between this paragraph and the next segment. The granularity must be the smallest possible value for the measure, and in the example of the sales order amount is 0.01. The end value of the last fragment is the maximum possible value for the sales orde

decision information. A major feature of ola p is multi-dimensional data analysis, which forms a combination and complementary relationship with the multi-dimensional data organization of the data warehouse. Ask this, use The combination of OLAP technology and data

Data Three CIF Case-sap BW The main feature is that ERP vendors provide the entire architecture, which saves a lot of design work, and reduces the cost of design and development, encapsulation of the business in BW, reducing the difficulty of long-term maintenance. ERP data resources are very rich and valuable, should be an important source of data warehousing,

this problem. From a development perspective, you can control the number of tables for joins. If you need to join the table too many, according to the classification of the business, the first round join, the number of tables to control within a certain range, and then get the first round of the join results, and then do a second round of global join, so there is no problem. From the operational perspective, you can set the Optimizer_search_depth parameter. It can control the depth of the join

This paper gives the basic concept of SQL Server Data Warehouse, and uses the example construction process to analyze, for everyone's reference! Basic concepts: 1. Cubes: Cubes are the primary object of online analytical processing (OLAP) and are a technology that allows fast access to data in a data