登录查看更多内容

What are some common challenges and pitfalls of using star schema or fact constellation?

由人工智能和领英社区提供技术支持

Dimensional modeling is a popular technique for designing data warehouses and business intelligence systems. It involves organizing data into facts and dimensions, which can be represented by different types of schemas, such as star schema or fact constellation. However, these schemas also have some challenges and pitfalls that you should be aware of before choosing them for your data modeling project. In this article, we will discuss some of the common issues and drawbacks of using star schema or fact constellation, and how to avoid or mitigate them.

此文章中的业界达人

由社区从 3 条内容中精选。了解更多

1 Data redundancy

One of the main advantages of using star schema or fact constellation is that they simplify the queries and analysis of data, as they reduce the number of joins and tables involved. However, this also comes at a cost of data redundancy, as the same dimension attributes may be repeated in multiple tables or fact tables. This can lead to increased storage space, maintenance effort, and data inconsistency. To avoid data redundancy, you should carefully design your dimensions and facts, and use techniques such as conformed dimensions, shared dimensions, or snowflaking.

添加您的观点

James Episale

CIO Chief Architect, Data Strategy - STSM
举报内容
In my experience any database design can have issues with data redundancy if not carefully thought out. I agree that carefully designed conformed dimensions and facts can help. In addition, making sure we have proper slowly changing conformed dimensions especially around master data to is important. With the careful implementation of a data fabric architecture and clear metadata management, redundancy can be better controlled at the sources of data minimizing the amount of transformation and redundancy need for data ecosystems.

已翻译

赞

2 Data granularity

Another challenge of using star schema or fact constellation is choosing the right level of data granularity for your facts and dimensions. Data granularity refers to the level of detail or aggregation of data, and it affects the performance, flexibility, and accuracy of your data warehouse. If your data is too granular, you may have too many rows and columns in your fact tables, which can slow down your queries and analysis. If your data is too aggregated, you may lose some important details or insights, and limit your ability to drill down or slice and dice your data. To choose the right level of data granularity, you should consider your business requirements, user expectations, and data sources.

添加您的观点

James Episale

CIO Chief Architect, Data Strategy - STSM
举报内容
Requirements should not be thought of in a vacuum. They should be looked at from the entire end to end data operations that they are a part of, and what is minimally needed to drive those data operations end to end. This ensures the correct amount of granularity and detail on your fact tables. This also helps minimize the amount of fact tables needed.

已翻译

赞

3 Data complexity

A third pitfall of using star schema or fact constellation is dealing with data complexity, especially when you have multiple fact tables or dimensions that are related to each other. Data complexity can arise from various factors, such as different levels of granularity, different time periods, different measures, different hierarchies, or different business rules. Data complexity can make your data modeling more challenging, as you have to ensure that your schemas are consistent, coherent, and compatible. To handle data complexity, you should use techniques such as bridge tables, role-playing dimensions, factless fact tables, or degenerate dimensions.

添加您的观点

John Lilley

Data Engineer at Motability Operations Ltd
举报内容
Slowly changing dimensions are one complexity that people really struggle to get right. Is it a type 1, 2 or 3? Do you really need the historical aspects captured in your data?

已翻译

赞

4 Data quality

A fourth issue of using star schema or fact constellation is ensuring the data quality of your data warehouse. Data quality refers to the accuracy, completeness, validity, consistency, and timeliness of your data, and it affects the reliability and usability of your data warehouse. Poor data quality can result from various sources, such as data errors, data gaps, data duplicates, data conflicts, or data changes. Poor data quality can undermine your data analysis and decision making, and cause user dissatisfaction and mistrust. To improve data quality, you should implement data quality checks, data cleansing, data validation, data reconciliation, and data governance.

添加您的观点

5 Data security

A fifth drawback of using star schema or fact constellation is securing the data in your data warehouse. Data security refers to the protection of your data from unauthorized access, modification, disclosure, or destruction. Data security is crucial for your data warehouse, as it may contain sensitive, confidential, or regulated data that could be exploited by malicious actors or competitors. Data security can be challenging for star schema or fact constellation, as they tend to expose more data to the users, and require more access controls and permissions. To enhance data security, you should use techniques such as encryption, authentication, authorization, auditing, and masking.

添加您的观点

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Dimensional Modeling

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

What are some common challenges and pitfalls of using star schema or fact constellation?

1

2

3

4

5

6

1 Data redundancy

2 Data granularity

3 Data complexity

4 Data quality

5 Data security

6 Here’s what else to consider

Dimensional Modeling

给文章评分

感谢您的反馈

更多Dimensional Modeling相关文章

更多相关阅读内容