degenerate dimension in data warehouse

IBM DB2 Data Warehouse Edition is a set of products that combine the strengths of DB2 Data servers and the robust business intelligence infrastructure from IBM. DB2 DWE integrates Core Components for warehouse management, data conversion,

of key historical changes to the entire enterprise data. Based on EDW, you can create Data Mart for different topics. Different enterprises in the Data Mart have different requirements, which can be divided into user topics, business flows, and product topics, there can be a lot of theme applications on the basis of EDW. It is very important to establish EDW. Th

level.Use DW; CREATE TABLE Month_dim ( month_sk INT comment ' surrogate key ', month tinyint comment ' month ', month_name Varc Har (9) Comment ' month name ', quarter tinyint comment ' quarter ', year smallint comment ' year ' ) Comment ' Month Dimension table ' clustered by (Month_sk) into 8 buckets stored as orc tblproperties (' transactional ' = ' true ');In order to import the month

considered from the point of view of the formation of the Data Warehouse) Δ Multi-tiered Data warehouse (data warehouse and data supermarket synthesis, the lower

, this is a data warehouse inevitable phenomenon, called star-type connection. Oh--in fact, these parts are named, the middle of the synthesis is the "fact table", the surrounding is a dimension table. And there is another phenomenon: the fact table contains the primary key of the dimension table. You may not have reac

ExploitationDimensionAdvanced Techniques for optimizing Data Warehouses Author:Anysql.netDuring reprinting, be sure to mark the original source and author information in the form of hyperlinks.Link:Http://www.anysql.net/Oracle/Oracle_Olap_dimension.html InOracleIn the data warehouse (OLAP), the materialized view (mview), query rewrite (query rewrit

. 3) consider how the data in the data warehouse is distributed across servers when the data volume rapidly increases to the point where the data in a single server cannot be stored, by topic, geographical location, or time? These policies have a significant impact on the

design is intended to introduce redundancy and adopt an anti-paradigm design. A database is designed to capture data. A data warehouse is designed to analyze data. Its two basic elements are dimension tables and fact tables. Dimensions are the definitions of these things, s

The index of the Data warehouse is a tricky issue. If there are too many indexes, the data is inserted quickly but the query response is slow. If too many indexes, the data import is slow and the data is more storage space, but the query responds faster. The role of indexes

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

 Multidimensional data modeling organizes data in an intuitive way and supports high-performance data access. Each multidimensional data model is represented by multiple multidimensional data patterns, and each multidimensional data