数据仓库专题(23):总线矩阵的另类应用-Drill Down into a More Detailed Bus Matrix

简介: 一、前言 Many of you are already familiar with the data warehouse bus architecture and matrix given their central role in building architected data marts.

一、前言

Many of you are already familiar with the data warehouse bus architecture and matrix given their central role in building architected data marts. The corresponding bus matrix identifies the key business processes of an organization, along with their associated dimensions. Business processes (typically corresponding to major source systems) are listed as matrix rows, while dimensions appear as matrix columns. The cells of the matrix are then marked to indicate which dimensions apply to which processes.

In a single document, the data warehouse team has a tool for planning the overall data warehouse, identifying the shared dimensions across the enterprise, coordinating the efforts of separate implementation teams, and communicating the importance of shared dimensions throughout the organization. We firmly believe drafting a bus matrix is one of the key initial tasks to be completed by every data warehouse team after soliciting the business’ requirements.

二、面临问题

While the matrix provides a high-level overview of the data warehouse presentation layer “puzzle pieces” and their ultimate linkages, it is often helpful to provide more detail as each matrix row is implemented. Multiple fact tables often result from a single business process. Perhaps there’s a need to view business results in a combination of transaction, periodic snapshot or accumulating snapshot perspectives. Alternatively, multiple fact tables are often required to represent atomic versus more summarized information or to support richer analysis in a heterogeneous product environment.

三、解决方案

We can alter the matrix’s “grain” or level of detail so that each row represents a single fact table (or cube) related to a business process. Once we’ve specified the individual fact table, we can supplement the matrix with columns to indicate the fact table’s granularity and corresponding facts (actual, calculated or implied). Rather than merely marking the dimensions that apply to each fact table, we can indicate the dimensions’ level of detail (such as brand or category, as appropriate, within the product dimension column).

 四、总结

The resulting embellished matrix provides a roadmap to the families of fact tables in your data warehouse. While many of us are naturally predisposed to dense details, we suggest you begin with the more simplistic, high-level matrix and then drill-down into the details as each business process is implemented. Finally, for those of you with an existing data warehouse, the detailed matrix is often a useful tool to document the “as is” status of a more mature warehouse environment.


作者:张子良
出处:http://www.cnblogs.com/hadoopdev
本文版权归作者所有,欢迎转载,但未经作者同意必须保留此段声明,且在文章页面明显位置给出原文连接,否则保留追究法律责任的权利。

相关文章
|
供应链 架构师 BI
数仓实践:总线矩阵架构设计1
数仓实践:总线矩阵架构设计1
1688 0
数仓实践:总线矩阵架构设计1
|
8月前
|
存储 芯片
快速入门数字芯片设计,UCSD ECE111(三)System Verilog时序逻辑(下)
快速入门数字芯片设计,UCSD ECE111(三)System Verilog时序逻辑(下)
59 0
|
8月前
|
存储 芯片 异构计算
快速入门数字芯片设计,UCSD ECE111(三)System Verilog时序逻辑(上)
快速入门数字芯片设计,UCSD ECE111(三)System Verilog时序逻辑
60 0
|
算法 芯片
METSO DPU-MR 映射工具寻址的最小功能单元
METSO DPU-MR 映射工具寻址的最小功能单元
111 0
METSO  DPU-MR 映射工具寻址的最小功能单元
|
机器学习/深度学习 资源调度 算法
ICA简介:独立成分分析
您是否曾经遇到过这样一种情况:您试图分析一个复杂且高度相关的数据集,却对信息量感到不知所措?这就是独立成分分析 (ICA) 的用武之地。ICA 是数据分析领域的一项强大技术,可让您分离和识别多元数据集中的底层独立来源。
251 0
Cosmos——Cosmos 有两条分离轴
Cosmos——Cosmos 有两条分离轴自制脑图
45 0
Cosmos——Cosmos 有两条分离轴
|
自然语言处理 数据可视化 大数据
谈谈如何从数据湖(Data Lake)架构转向数据网格(Data Mesh)架构
尽管数据网格实践被应用在有些客户中,但企业规模性的采用仍有很长的路要走。
谈谈如何从数据湖(Data Lake)架构转向数据网格(Data Mesh)架构
|
机器学习/深度学习 存储 数据挖掘
NumPy数据分析基础:数组形态转换转置操作一文详解
NumPy数据分析基础:数组形态转换转置操作一文详解
160 0
NumPy数据分析基础:数组形态转换转置操作一文详解
|
架构师 数据管理 BI
数仓实践:总线矩阵架构设计2
数仓实践:总线矩阵架构设计2
1165 0
|
机器学习/深度学习 Ubuntu TensorFlow
Graph-Learn(GL,原AliGraph) 面向大规模图神经网络的研发和应用而设计的一款分布式框架
它从实际问题出发,提炼和抽象了一套适合于当下图神经网络模型的编程范式, 并已经成功应用在阿里巴巴内部的诸如搜索推荐、网络安全、知识图谱等众多场景。