olap in data warehouse

Schema | Data Microsoft Data Warehouse architecture Absrtact: This article briefly introduces the data warehouse using the Microsoft Data Warehouse architecture, discusses the function

Introduction With the rapid development of China's financial industry, it is an urgent problem for the bank to support the decision analysis by classifying and storing the customer data, analyzing the automatic discriminant of the various factors, and digging out the information needed by the bank managers and the business analyst in the presence of a large amount of data.

Before you start spraying this topic, let's take a look at the official definition of the Data warehouse:数据仓库(Data Warehouse)是一个面向主题的(Subject Oriented)、集成的(Integrate)、相对稳定的(Non-Volatile)、反映历史变化(Time Variant)的数据集合，用于支持管理决策。以上是数据仓库的官方定义。"Operational database" such as the Bank accounting system database, every business op

the attribute of the indicator. For example: "2015-01-12 PV is 1000", then the date (is the 2015-01-12 abstract) is the dimension, PV is the indicator, 1000 is the valueLatitude tableThe dimension table puts the data table that holds the dimension, or the data table of the dimension relationshipReal-time TablesThe fact table holds the data to query the dimension

Why layer data warehouses? Use space for time and a large amount of preprocessing to improve the user experience (efficiency) of the application system. Therefore, a large amount of redundant data exists in the data warehouse; If there is no hierarchy, changing the business rules of the Source Business syste

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 that holds a detailed value or fact for a measure is called a fact table. A fact table that s

Fact tableIn a multidimensional data warehouse, a table that holds a detailed value or fact for a measure is called a fact table. A fact table that stores sales and sales by state, product, and month has 5 columns that are conceptually similar to the following example. Sate Product Mouth Units Dollars WA Mountain-100 January 3 7.95

Overview This article discusses the role of data partitioning in data warehouses. Relational data warehouses and Analysis Services cubes support data partitioning. The logical concept of partitioning is the same in the two engines of Microsoft®sql Server™: Horizontal partitioning of

Directory Objective Principle Content Summary Preface The previous article described the business Modeling and domain concept modeling in the Data warehouse model design, and then came naturally to the phase of the logical Data Modeling LDM (Logical data Model), which is on

About star schema In the construction of the Data Warehouse, the star pattern shown in the following illustration is almost the most commonly used. It is called star mode because the E-R graphic in the pattern is like a star (it feels strange to say). NBSP as shown in the figure, the center is a large fact table with some dimension tables around it. The fact table contains the primary information fo

Policy | data 6 Strategies of Data Warehouse implementation In the implementation of the data warehouse, the need for theoretical guidance, with the development of data Warehouse t

ETL Tools: IBM Datastage Informatica PowerCenter Teradata ETL Automation OLAP (On-line Analytical Processing) Microsoft related products: SSAS Olap--rolap--molap Related (to find): OLAP (On-line analysis processing) is a kind of software technology that enables analysts, managers, or executives to access information quickly, consistently and interactively

Tags: des style blog http color os using IO JavaFor the first two years of the Data Warehouse, all calculations (including data ETL, and report calculation) were implemented using a high-profile standalone + MySQL approach. No OLAP). Used MySQL's own MyISAM and Columnstore engine infobright. This article summarizes som

warehouses, as follows:In general, I agree with the new generation of data warehousing, which is easy to use, efficient, extensible, data sharing, etc., but it is difficult for me to disagree with the comparison, especially in the speed, expansion two. Traditional Data Warehouse, the size of the