You can use the Looker API to create a data dictionary based on the definitions and descriptions of fields defined in the Looker modeling layer. Creating a data dictionary can be useful for:

• Creating a reference source for all consumers of external reports.
• Providing a location for business users to search all metrics descriptions to identify the appropriate dimension or measure for analysis.
• Making it easy for external stakeholders to identify and locate the unique metrics for their business.
• Auditing LookML models to assess whether consistent naming conventions are followed, whether there are redundant fields, or if fields are annotated with descriptions.

Step 1:

Authenticate into the API following the steps outlined in the Authentication with an SDK section of the Looker API Authentication documentation.

Step 2:

Define fields with descriptions and other parameters within the model in your Looker instance.

Step 3:

Call the lookML_model_explore endpoint via the API and pull in the appropriate fields you want to identify within your data dictionary.

Here is a sample Ruby code that puts the above steps all together:

        #Get Data Dictionary

        require 'looker-sdk'

        module ApplicationHelper

            def self.api_auth
              sdk = LookerSDK::Client.new(
              # Looker/API Credentials
                :client_id => ENV['API_CLIENT_ID'],
                :client_secret => ENV['API_SECRET'],
                :api_endpoint => ENV['API_ENDPOINT']
              )
              return sdk
            end

            def self.get_field_values(model_name, explore_name)

                sdk = self.api_auth()
                fields = {:fields => 'id, name, description, fields'}

                #API Call to pull in metadata about fields in a particular explore
                fields = sdk.lookml_model_explore(model_name, explore_name, fields)


                my_fields = []

                #Iterate through the field definitions and pull in the description, sql, and other looker tags you might want to include in  your data dictionary.
                fields[:fields][:dimensions].to_a.each do |x|
                    dimension = {
                        :field_type => 'Dimension',
                        :view_name => x[:view_label].to_s,
                        :field_name => x[:label_short].to_s,
                        :type => x[:type].to_s,
                        :description => x[:description].to_s,
                        :sql => x[:sql].to_s
                    }
                    my_fields << dimension
                end

                fields[:fields][:measures].to_a.each do |x|
                    measure = {
                        :field_type => 'Measure',
                        :view_name => x[:view_label].to_s,
                        :field_name => x[:label_short].to_s,
                        :type => x[:type].to_s,
                        :description => x[:description].to_s,
                        :sql => x[:sql].to_s
                    }

                    my_fields << measure
                end

                return my_fields
            end

        end
    

Step 4:

Format the results from the get_field_values call. Consider using plugins to allow for search and sort functionality. In our example, we use the DataTable JS plugin.

Examples:

Default Dictionary with Pagination and Sorts:

Dictionary Filtered to Fields that Contain the Word lifetime:

Additional information on data dictionaries combining the API and Python to output a data dictionary to .csv can be found here in the Community post Writing a Simple Data Dictionary to CSV Using the Looker API and the Python requests library. Another example for using the API to output a data dictionary to Google Sheets can be found in the Help Center article Generating a Data Dictionary in Google Sheets.