登录查看更多内容

How can you effectively name tables and columns in a snowflake schema?

由人工智能和领英社区提供技术支持

A snowflake schema is a type of data warehouse design that organizes data into hierarchical dimensions and facts. It is similar to a star schema, but with more normalized tables that reduce data redundancy and improve query performance. However, having more tables and columns also means that you need to follow some best practices for naming them, so that you can easily identify and access the relevant data. In this article, you will learn how to effectively name tables and columns in a snowflake schema, using some common conventions and guidelines.

本文章的要点总结

Descriptive consistency:

Adopting descriptive and consistent naming for tables and columns clarifies their purpose. Use names like Customer_Dimension, not Table1, to make it easy to understand data at a glance.
Incorporate metadata:

Enhance your data's clarity by including metadata and comments within your schema. This extra layer of information demystifies the data, offering insights into its usage and origin.

本摘要由 AI 和以下专家提供支持

1 Use descriptive and consistent names

One of the most important rules for naming tables and columns in a snowflake schema is to use descriptive and consistent names that reflect the meaning and purpose of the data. For example, you should avoid using generic names like Table1 or ColumnA, or names that are too long or too short. Instead, you should use names that clearly indicate the type, level, and content of the data, such as Customer_Dimension, Order_Fact, or Product_Category. You should also use the same naming convention for all tables and columns, such as using singular or plural nouns, uppercase or lowercase letters, underscores or camel case, and so on.

添加您的观点

Kadhirvelu Ratnasabapathi

VP at Seacoast Bank, LinkedIn Top Voice: | Data Governance | Data Quality | Data Management | Data Modeling | Business Analysis | Business Intelligence | Data Warehousing | Data Architecture | Master Data Management
举报内容
Naming conventions should be established much ahead of (the time before) building snowflake schema as part of the Data Warehouses. Typically it is done by (Enterprise) Data Architecture team and they (should) approve any names for tables and columns, as they possess the holistic view and the responsibility for (consistent) standards. Acronyms and abbreviated names, allowed special characters can be used while naming, that are defined as part of the organization standards.

已翻译

赞
Praveen Karader

Data Architect | Microsoft Certified Trainer & Power BI Data Analyst | LinkedIn Top Voice | 13 years into Data | I help people succeed in data field.
举报内容
When naming tables and columns in a Snowflake Schema, adopt a systematic approach for clarity and consistency. Use descriptive, meaningful names that convey the table's or column's purpose. Employ a prefix to categorize tables based on their function or module, ensuring logical organization. Employ underscores for spaces, avoiding special characters for compatibility. Choose concise yet expressive names, avoiding abbreviations that may lead to confusion. Incorporate business terminology for better understanding across teams. Consistent naming conventions enhance schema readability, making data exploration and maintenance more efficient in the Snowflake model.

已翻译

赞
Oleh Khrol

Senior Data Engineer
(已编辑)
举报内容
As for columns, consider using the same name as ones that you use in dashboards. It is boring to always rename Product_Category to "Product Category" in a query or in a dashboard settings. What if using "Product Category" as a column name would save you a lot of time and reduce the chance of making a bug?

已翻译

赞

2 Use prefixes and suffixes

Another useful technique for naming tables and columns in a snowflake schema is to use prefixes and suffixes that indicate the role and structure of the data. For example, you can use prefixes like Dim_ or Fact_ to distinguish between dimension and fact tables, or suffixes like _Key or _ID to identify primary and foreign keys. You can also use prefixes or suffixes to indicate the level of granularity or aggregation of the data, such as Daily_, Monthly_, or Total_. Using prefixes and suffixes can help you to organize and sort your tables and columns, and to avoid name conflicts or ambiguities.

添加您的观点

Mohammed Haris khan

Data Engineer at GlobalLogic
(已编辑)
举报内容
As a best practice, all dimensions should have the prefix 'Dim_' and all facts should have the prefix 'Fact_'. This, along with descriptive names, helps identify whether the table is a dimension or a fact just by looking at table names. For facts, along with the prefix , we can have a suffix based on the aggregation level in the fact. The same transaction data can be aggregated at monthly, daily or yearly level and populated in the respective facts. Putting _monthly, _daily etc as a suffix will avoid ambiguity in such cases and will also help identify the level of aggregation in the respective fact table .

已翻译

赞

3 Use abbreviations and acronyms sparingly

Sometimes, you may need to use abbreviations or acronyms to shorten the names of tables and columns in a snowflake schema, especially if they are too long or contain common terms. However, you should use them sparingly and only when they are widely understood and accepted. For example, you can use abbreviations like Qty for Quantity, or acronyms like SKU for Stock Keeping Unit, but you should avoid using obscure or ambiguous ones that may confuse or mislead the users. You should also document and explain the abbreviations and acronyms that you use, and make sure that they are consistent and logical.

添加您的观点

4 Use metadata and comments

Finally, you should use metadata and comments to provide additional information and context for the tables and columns in a snowflake schema. Metadata is data about data, such as the data type, format, length, source, and description of each table and column. Comments are textual annotations that you can add to the table and column definitions, such as the business rules, assumptions, calculations, or references that apply to the data. Using metadata and comments can help you to document and communicate the meaning and quality of the data, and to facilitate the data analysis and reporting.

添加您的观点

Mohammed Haris khan

Data Engineer at GlobalLogic
(已编辑)
举报内容
Creating metadata, data dictionaries and source to target mapping documents are of significant importance in data warehouse implementation. Data dictionaries help with the information about the data and its real world's meanings a table and columns hold. STMs (Source to target mapping) documents the information about the transactional table source of the dimension/facts tables and the columns. These documents help implementation/development teams keep a track of data stored in the data warehouse and help them look back to this information when needed. Also, new members joining the team get the utmost help from referring to these documents.

已翻译

赞

5 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

Kadhirvelu Ratnasabapathi

VP at Seacoast Bank, LinkedIn Top Voice: | Data Governance | Data Quality | Data Management | Data Modeling | Business Analysis | Business Intelligence | Data Warehousing | Data Architecture | Master Data Management
举报内容
Apart of tables and columns, other database objects like indexes, constraints, Etc.. should also be considered within naming conventions radar. It is mandatory to make sure these objects get into metadata repository and/or data catalogs. The descriptions and contexts can also be added while creating the objects.

已翻译

赞

Data Warehousing

+ 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

How can you effectively name tables and columns in a snowflake schema?

1

2

3

4

5

1 Use descriptive and consistent names

2 Use prefixes and suffixes

3 Use abbreviations and acronyms sparingly

4 Use metadata and comments

5 Here’s what else to consider

Data Warehousing

给文章评分

感谢您的反馈

更多Data Warehousing相关文章

更多相关阅读内容

How can you effectively name tables and columns in a snowflake schema?

1

2

3

4

5

1 Use descriptive and consistent names

2 Use prefixes and suffixes

3 Use abbreviations and acronyms sparingly

4 Use metadata and comments

5 Here’s what else to consider

Data Warehousing

给文章评分

感谢您的反馈

查看其他技能