Read about degenerate dimension in data warehouse, The latest news, videos, and discussion topics about degenerate dimension in data warehouse from alibabacloud.com
The original: "Bi thing-the art of data" understanding Dimension Data Warehouse-fact table, dimension table, aggregation tableFact tableIn a multidimensional data warehouse, a table tha
tables will have different measures. A sales data warehouse may contain these two measure columns: Sales and sales. A field information Data Warehouse may contain 3 measure columns: Total, number of minutes, and number of defects. When you create a report, you can think of a measure as an extra
0x00 Preface The following content, is the author in the study and work of some summary, of which the concept of most of the content from the book, the practical content mostly from their own work and personal understanding. Due to the lack of qualifications, there will inevitably be many mistakes, I hope to criticize. Overview The Data warehouse contains a lot of content, which can include architecture, m
business dimension information for integrated integration, this situation is more appropriate for generating surrogate keys to master keys. Summary The construction of the dimension table seems relatively simple, in most cases the business library will be directly, but in addition to the different levels of the dimension of Redundancy (Star model), b
ALPHA Mid-High 5500.01 6500.00 PROJECT ALPHA Top 6500.01 99999999.99 Grid Low 0.01 3000.00 Grid MED 3000.01 6000.00 Grid High 6000.01 99999999.99 Table (v)-16-1 Each fragment has a start value and an ending value. The granularity of a fragment is the gap between this and the next paragraph. The granularity must be the minimum possible value for the metric, which is 0.01 in the exam
product Dimension has a product family, a large class, and a small class of three attributes, which defines [ classify layer level, take advantage of these three properties directly, That is, each level is a property of a member. The other is between the dimension members, such as hr , each level is a specific dimension member, that is, each level is one or
In the process of extracting data from an OLTP business database to a DW data warehouse, especially after the first import, the problem is that some data in the business database has been changed to reflect these changes to the Data ware
Dimension Modeling Method Dimension modeling organizes information into structs, which typically correspond to the query methods that analysts want to use for data warehouse data. How much food sales were in the northwest in the third quarter of 1999. Represents the use of
Warehouse. From here you can see that it has several features:1. The redundancy of the dimension tables is large, mainly because the dimensions are generally small (relative to the fact table), and the redundancy of the dimension tables can save a lot of space in the fact table. 2. Fact sheets are generally very large, and if queried in an ordinary way, the time
We often encounter this problem in the design of the Data Warehouse: If the dimension has only one attribute in the dimension design, is the choice to create a single dimension for this attribute, or will the attribute of that dimension
On the theoretical concept of slowly changing Dimension slowly changing dimension see Data Warehouse Series-Slow slowly changing dimension (slowly changing Dimension) common three types and prototype design This article summarize
Using the Javadate class Data Warehouse dimension tableDate Category:, returns the number of milliseconds for a relative date. Accurate to milliseconds. However, the internationalization and sub-timezone display of dates is not supported.The date class began to evolve from the Java Development Package (JDK) 1.0, when it included only a few ways to get or set the
Use the java date class to generate a data warehouse dimension table Use the java date class to generate a data warehouse dimension table Date class: Returns the number of milliseconds of a relative date. Accurate to milliseconds,
Generating a Data Warehouse dimension table using the Java date classDate class:The most basic date-time class that returns the number of milliseconds for a relative date. Accurate to milliseconds, but does not support the internationalization and sub-timezone display of dates. The date class began to evolve from the Java Development Package (JDK) 1.0, when it co
. 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, such as time, department, and dimension tables. The fact table contains the data
data redundancy caused by the problem is not the reason to apply the database paradigm. Therefore, not the higher the application paradigm, the better, depends on the actual situation. The third paradigm has largely reduced data redundancy and reduced the number of insertions, updates, and deletions. My personal view is that most of the cases applied to the third paradigm are sufficient, and in some cases
, subtract, or multiply these columns. Dimensions are more based on themes. In this example, you have athlete information dimensions, time and date dimensions, and so on. Columns in multiple dimensions are usually not computed or weighted. In this example, the key that connects a dimension table to a fact table is playerID. In simple terms, sometimes you need to use the tools that are in front of you. Anyone who has worked on IT for some time may
analytical processing): Online Analytical Processing OLAP was proposed by E. F. codd in 1993.Definition by the OLAP Council: OLAP is a software technology that enables analysts to quickly, consistently, and interactively observe information from various aspects to gain an in-depth understanding of data, this information is directly converted from raw data. They reflect the real situation of the enterprise
Analytical Processing): Online Analytical ProcessingOLAP was proposed by E. F. Codd in 1993.Definition by the OLAP Council: OLAP is a software technology that enables analysts to quickly, consistently, and interactively observe information from various aspects to gain an in-depth understanding of data, this information is directly converted from raw data. They reflect the real situation of the enterprise i
content Physical Modeling: This part of modeling mainly includes the following parts: Make technical adjustments for specific physical platforms Make adjustments to specific platforms for model performance considerations Make corresponding adjustments based on specific platforms for management needs Generate and complete the final execution script. From the perspective of the division of various stages in the Data Mod
Start building with 50+ products and up to 12 months usage for Elastic Compute Service
1 on 1 presale consultation
24/7 Technical Support 6 Free Tickets per Quarter Faster Response
Alibaba Cloud offers highly flexible support services tailored to meet your exact needs.
A comprehensive suite of global cloud computing services to power your business
Payment Methods We Support
