data warehouse semantic layer

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Overview of dimension model Data Warehouse base objects Concept

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 more dimension members, Each level contains multiple properties. The latter level is often

My view of Data Warehouse (design article)

) and underlying modeling (physical layer). The order of construction is up and down, as if we were sitting together, discussing a general structure, starting the design of the middle tier (because the data that red needs is not easy to extract, requires a certain combination of methods), and then designing the underlying model based on the middle tier, (data fro

SQL Server Data Warehouse concepts and building process

data warehouse model involves two parts. One is to consider the useful data that the original data source can provide, that is, the data can be used for the data warehouse after the

The construction and analysis of SQL Server Data Warehouse

what the original data source can provide useful data, that is, after the data filter can be used for the Data warehouse. The second is to see what the company's business layer needs analysis results. This should work closely wit

SQL Server Data Warehouse Construction and Analysis

Instance construction process and analysis 1Now we will analyze and discuss it with a simple example.Ms SQL ServerData warehouse construction process. In fact, the construction of the Data Warehouse is quite complex. It combines the front-end technology of the data warehouse

The difference between a database and a data warehouse

information can lead to analysis of the wrong decision, resulting in loss, Rather than benefits.3. Extensibility. The reason why some large Data Warehouse system architecture design is complex, because of the expansion of the next 3-5 years, so that customers do not have to spend too quickly to rebuild the Data Warehouse

Introduction to the DB2 data warehouse OLAP Service

hierarchy, a multi-dimensional dataset layer, and a fact object of a multi-dimensional dataset. For each dimension of a multi-dimensional dataset, only one hierarchy can be defined for one cube. However, in a multi-dimensional dataset model, a dimension can have multiple hierarchies. You can use a multi-dimensional dataset to optimize the multi-dimensional dataset model so that it can be more adapted to the most active and important multi-dimensional

BI architecture and data warehouse design

BI architecture and data warehouse design (14:26:41)BI architecture and data warehouse design A data warehouse solution is often related to a large Bi strategy. When you plan to use the fast tracing architecture, the most impo

Open-source MySQL efficient data warehouse solution: Infobright details _ MySQL

This article mainly introduces the open-source MySQL efficient data warehouse solution: Infobright details. This article describes the features of Infobright, the value of Infobright, the applicable scenarios of Infobright, and the comparison with MySQL, for more information, see Infobright, a column-based database based on the unique patented knowledge grid technology. Infobright is an open-source MySQL

Open-source MySQL efficient data warehouse solution: Infobright details, mysqlinfobright

Open-source MySQL efficient data warehouse solution: Infobright details, mysqlinfobright Infobright is a column-based database based on unique patented knowledge grid technology. Infobright is an open-source MySQL Data Warehouse solution that introduces the column storage solution, high-strength

Glossary: Data Warehouse

designed, implemented, and the results must be consistent, data and methods must be stored in a single, globally acceptable format. Only in this way can DSS use the data without worrying about the consistency of the data. Third, it is historic and reflects historical changes. Operational databases are mainly concerned with d

Implementation method of lightweight Data Warehouse

the interface between the database, the response speed will be worse, any good software, this response speed is intolerable. The amount of data collected in network management is huge, the number of packets sent by the millions in the statistics IP layer, the number of bytes, the monitoring of the important or the status information of the core equipment is common. In the case of network management softwar

A review of data warehouse and OLAP Technology

Server (Middle Layer)-data warehouse server (bottom layer ). The underlying data warehouse server is usually a relational database system. The middle-layer OLAP Server is typically imp

The architecture of the Data Warehouse

According to the author's experience in the Data Warehouse ETL internship in Teradata and Main Street network, we can understand the relationship and difference between them in architecture design. Teradata is generally the enterprise-class data Warehouse, in the Teradata Data

Data warehouse architecture

. This is what many vendors once talked about. The two methods can be used flexibly based on different needs. Most db software uses row-based storage, and column-based storage features efficient single-column value compression. When selecting fewer columns, the io requirement is very low and the speed is fast, however, the compression efficiency of Row-store databases is also rapidly improved. Most of the requirements are to select row data for observ

Open-source MySQL Data Warehouse solution: Infobright

Infobright is a column-based database based on unique patented knowledge grid technology. Infobright is an open-source MySQL Data Warehouse solution that introduces the column storage solution, high-strength data compression, and optimized Statistical Computing (Class Infobright is a column-based database based on unique patented knowledge grid technology. Infobr

Building a Data Warehouse No. 04: A Summary of logical modeling –1–

Data Warehouse different levels have different granularity, DW layer is the data is atomic granularity of data, such as transaction data atomic granularity is an order, records also include the purchase of users and merchant

Model design of Data Warehouse

example:Figure 1: Star-shaped modelFigure 2 Snowflake ModelFrom the performance point of view, Star Model query performance is good.To improve performance, you can allow for violations of the third paradigm, appropriate redundancy, and hiding the constraints between tables.Dimension modelingThe business dimension is fused into the data model, and the name dimension is modeled.Or, for analysis convenience (commercial application requirements), the dif

Data Warehouse Series-Why dimensions are modeled

approach, that is, "snowflake model", while also the seriousness of the comparison between the Snowflake model and star model (i.e. the above example) of the pros and cons, so that many beginners heart floated a lingering doubt , "Since the snowflake model has the advantages of the relational model and the advantages of the dimension model, why should there be a star model?" stealing thought, this is completely superfluous the practice, the dimension modelling pursues is uses simple, increases

Model design __DW of Data Warehouse

layers: interface layer, intermediate summary layer and application layer. Application layer data mart City Data Mart, data mining appl

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