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What are Dimensions?

Dimensions offer a way to group or filter data based on categories or timeframes. Think of them as special labels that simplify data organization and analysis. Within data platforms, dimensions are integral components of a semantic model, alongside identifiers and measures.

In SQL, dimensions typically align with the group by clause of your SQL query.

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How to Define Dimensions

Each dimension requires a name and type, and may include an expression parameter. Key parameters include:

Name

The display name for the dimension. It can serve as an alias if the column name or SQL query reference (in expr) differs.

Type

Defines the dimension’s grouping nature in the semantic model (e.g., Categorical, Time).

Time granularity

For Time dimensions, specifies the granularity for grouping metrics (e.g., day, week, month).

Description (optional)

Provides a detailed explanation of the dimension.

Expression (optional)

Specifies the underlying column or SQL query for the dimension. Defaults to the dimension’s name if expr is omitted.

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How to create and edit a dimension

In the Catalog section, select the table for which you want to configure a dimension. Then, navigate to the Dimensions tab.

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Specification for Dimensions

Dimensions are logically defined using the following parameters:

dimensions:
  - name: [name]
    type: [Categorical or Time]
    type_params: [specific type parameters]
    description: [description]
    expr: [column_name_or_sql_expression] # Defaults to dimension name if omitted

For example, in a semantic model for transactions:

semantic_models:
  - name: transactions
    description: A record of every transaction. Carts are grouped as multiple transactions per SKU.
    model: {{ ref("fact_transactions") }}
    defaults:
      agg_time_dimension: metric_time
  dimensions:
    - name: metric_time
      type: time
      expr: date_trunc('day', ts)
    - name: is_bulk_transaction
      type: categorical
      expr: case when quantity > 10 then true else false end

Note: To correctly identify and process dimensions, each dimension must be associated with a primary identifier.

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Types of Dimensions

Findly supports two primary types of dimensions: Categorical and Time.

Categorical dimensions facilitate grouping metrics by categories, like product type or geographical region. They can reference existing columns or be derived from SQL expressions.

Example:

dimensions:
  - name: is_bulk_transaction
    type: categorical
    expr: case when quantity > 10 then true else false end

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Wrapping Up

Dimensions play a pivotal role in organizing, filtering, and analyzing your data. By mastering the use of dimensions, you can create more meaningful and insightful data models.