Associate Data Practitioner

Manipulate simple LookML parameters to modify a data model

Define and Apply LookML Parameters

LookML parameters are the core elements you use to model data in Looker. These parameters live in view files and model files to tell Looker how to query and display data. By adjusting simple parameters, you can customize the data model for specific analytics needs. Understanding these building blocks is critical for creating reliable Explores and dashboards.

Dimensions describe raw data attributes that users can pick and filter in an Explore. A typical dimension uses this syntax:

• dimension: the field name
• type: the data format (e.g., string, number, time)
• sql: the SQL expression referencing the table
  You can also set group_label to organize fields and primary_key to mark unique identifiers in your data.

Measures perform calculations or aggregations, like counts or sums, on your dataset. You declare a measure with:

• measure: the calculation name
• type: the aggregation method (count, sum, average)
• sql: (optional) a custom SQL expression
• filters: (optional) criteria to narrow the data
  You can also specify drill_fields to let users explore the details behind summary values.

Explores define entry points for queries in a model file and may join multiple views:

• explore: the primary data subject
• join: the view to connect
• type: join style (left_outer, inner, etc.)
• sql_on: the join condition
• relationship: cardinality (for example, one_to_many)
  Using proper Explores lets dashboard authors combine fields across views for richer analyses. Substitution operators like ${TABLE} and ${field_name} help avoid repeating code and simplify updates.

Conclusion

In this section, you learned how simple LookML parameters allow you to shape your data model by defining dimensions, measures, and Explores. By adjusting these parameters, you can customize how Looker queries and presents your data. Mastering these elements ensures your data model meets business requirements for accuracy and clarity.

Dimensions capture the raw attributes users filter on, measures calculate and aggregate key metrics, and Explores join views into cohesive starting points for analysis. Each component uses a clear syntax and optional settings to enhance organization and usability. Together, they form a flexible model that supports reliable visualizations.

Finally, using substitution operators and consistent parameter definitions helps you build a maintainable LookML model. This approach saves time when updating models and ensures your dashboards stay accurate. Mastering these concepts lays a strong foundation for creating insightful and dependable data analyses in Looker.