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

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.

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.

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?

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 .

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.

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.