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Overview

Data warehouse naming conventions play an important role in ensuring that a user’s data exploration and modeling experience is clear, consistent, and efficient.

The building blocks outlined below help create a structured, methodical approach to naming tables and columns, making it easier for users to understand and navigate what is typically inconsistent and unpredictable. We use these building blocks to create consistent formulas that populate the columns and tables in your data warehouse.

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Naming convention building blocks

  • entity: An entity refers to the primary object or concept that a column or table is related to. It represents the main subject of the data stored in the column or table, such as an order, customer, or product.
  • metric: A metric is a quantifiable measure that is used to track and assess the status of a specific process or activity within an entity. It often represents numerical data, such as financial figures (e.g., revenue, costs), counts (e.g., order counts, customer counts), or other measurable attributes of an entity.
  • dimension: A dimension provides context to data and is often used for grouping, segmenting, and categorizing data. It is an attribute or qualifier of an entity that allows data to be sliced and diced for analytical purposes. Dimensions are non-quantifiable attributes that describe aspects of the entity, such as dates, geographical information, or categorical data like product types or customer segments.
  • modifier: A modifier is an additional qualifier that provides extra context or specificity to a metric or dimension. It can be used to refine or alter the meaning of a metric or dimension by specifying a particular condition, state, or variation.
  • prefix: A prefix is a string of characters added to the beginning of a column name to provide additional context, indicate a specific source, or denote a particular type of data.
  • suffix: A suffix is a string of characters added to the end of a column name to provide additional information about the type of data the column contains or the format of the data.

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Overview

Data warehouse naming conventions play an important role in ensuring that a user’s data exploration and modeling experience is clear, consistent, and efficient.

The building blocks outlined below help create a structured, methodical approach to naming tables and columns, making it easier for users to understand and navigate what is typically inconsistent and unpredictable. We use these building blocks to create consistent formulas that populate the columns and tables in your data warehouse.

​
Naming convention building blocks

  • entity: An entity refers to the primary object or concept that a column or table is related to. It represents the main subject of the data stored in the column or table, such as an order, customer, or product.
  • metric: A metric is a quantifiable measure that is used to track and assess the status of a specific process or activity within an entity. It often represents numerical data, such as financial figures (e.g., revenue, costs), counts (e.g., order counts, customer counts), or other measurable attributes of an entity.
  • dimension: A dimension provides context to data and is often used for grouping, segmenting, and categorizing data. It is an attribute or qualifier of an entity that allows data to be sliced and diced for analytical purposes. Dimensions are non-quantifiable attributes that describe aspects of the entity, such as dates, geographical information, or categorical data like product types or customer segments.
  • modifier: A modifier is an additional qualifier that provides extra context or specificity to a metric or dimension. It can be used to refine or alter the meaning of a metric or dimension by specifying a particular condition, state, or variation.
  • prefix: A prefix is a string of characters added to the beginning of a column name to provide additional context, indicate a specific source, or denote a particular type of data.
  • suffix: A suffix is a string of characters added to the end of a column name to provide additional information about the type of data the column contains or the format of the data.