How to Use LookML in Looker Studio

Building rich, interactive reports often requires more than just dragging and dropping fields onto a canvas. Looker's modeling language, LookML, is one of the most powerful tools for shaping, enriching, and standardizing your business data before it ever reaches a dashboard. This article breaks down what LookML is, how it actually relates to Looker Studio, and how you can use it to build more robust and reliable reports.

What Exactly is LookML?

LookML (Looker Modeling Language) is not a general-purpose programming language, but rather a dependency-based language used to describe your data. Think of it as a set of instructions that tells Looker how to interpret your database schemas. Instead of writing raw SQL in every single chart, you use LookML to create a reusable data model that acts as a single source of truth for your entire organization.

In this model, you define distinct business entities like "customers," "orders," and "products," and describe the relationships between them. You define dimensions (groupable attributes like Order Date or Customer City) and measures (aggregate calculations like Total Revenue or Average Order Value).

The core benefits of this approach are:

• <strong>Consistency:</strong> The definition for "revenue" is written once in the LookML model. Every single person in the company who uses that field in a report is using the exact same calculation, eliminating discrepancies.
• <strong>Reusability:</strong> Once a dimension or measure is defined, it can be used in any number of reports and dashboards without rewriting the logic.
• <strong>Abstraction:</strong> Your end-users don't need to know SQL. They just interact with clean, well-defined fields like "User Lifetime Value," while all the complex SQL logic is handled behind the scenes in the LookML code.

The Big Confusion: LookML Doesn't Live in Looker Studio

Here's the most important thing to understand right away: you don't write LookML directly within Looker Studio. The two products, while both part of the Google Cloud family, serve different purposes in the data analysis workflow.

• <strong>Looker</strong> is the enterprise-grade business intelligence platform where you build the sophisticated data model using LookML. It connects directly to your database, and developers use LookML to structure and define all the business logic.
• <strong>Looker Studio</strong> (formerly Google Data Studio) is the data visualization tool. It’s excellent for creating user-friendly, shareable dashboards.

So, how do they work together? You create your robust, governed data model in Looker using LookML. Then, you connect Looker Studio to your Looker instance. Looker Studio uses the polished, pre-defined LookML model as its data source. This gives you the best of both worlds: the power and governance of Looker's data modeling layer, and the ease-of-use and flexibility of Looker Studio's visualization interface.

Getting Started: Your LookML to Looker Studio Workflow

The process involves preparing your data model in Looker first. It can be broken down into a few key steps focused on structuring your LookML project logically.

Step 1: Understand the Key Files in a LookML Project

A LookML project is typically managed in a Git repository and is composed of several types of files that work together:

• <strong>Model Files (.model.lkml):** These are the central hubs of your project. A model file specifies the database connection and defines a collection of "Explores," which are the query starting points for business users.
• <strong>View Files (.view.lkml):** A view file typically describes a single database table (or a derived table). This is where you'll spend most of your time, defining all the dimensions and measures associated with that table. For instance, you’d have orders.view.lkml to describe your orders table and users.view.lkml for your users table.
• <strong>Dashboard Files (.dashboard.lookml):** You can also define dashboards as LookML code, a practice known as "LookML Dashboards." These are version-controlled and tightly integrated within the Looker environment.

Step 2: Define Dimensions and Measures in a View

This is where the magic really begins. Let’s say we are working with an orders table. You would create an orders.view.lkml file to define what an "order" is in your business and what you can measure about it.

A dimension is a groupable field. It could be a column in your table, like status or user_id, or a transformed column, like the date or week of an order.

A measure is an aggregation of data, like a count, sum, or average. Unlike dimensions, measures are calculated across multiple rows.

Look at this simple example for an orders view:

view: orders {
  sql_table_name: public.orders ,,

  dimension: id {
    primary_key: yes
    type: number
    sql: ${TABLE}.id ,,
  }

  dimension_group: created {
    type: time
    description: "The date and time the order was placed."
    timeframes: [raw, date, week, month, quarter, year, fiscal_month_num]
    sql: ${TABLE}.created_at ,,
  }

  dimension: status {
    type: string
    sql: ${TABLE}.status ,,
    description: "Current status of the order (e.g., complete, pending)."
  }
  
  dimension: user_id {
    type: number
    sql: ${TABLE}.user_id ,,
  }

  measure: order_count {
    type: count
    label: "Total Number of Orders"
    description: "The total volume of orders."
  }

  measure: total_revenue {
    type: sum
    sql: ${TABLE}.sale_price ,,
    value_format_name: usd
    label: "Total Revenue"
  }
}

Step 3: Define Joins and Create Explores in Your Model File

A single table is useful, but business insights come from connecting data. In your .model.lkml file, you create "Explores." An Explore is a logical collection of joined views that users can browse and query from the Looker UI.

For example, if you want to analyze orders by user information, you need to join your orders view to a users view. You would define this relationship within an explore block in your model file.

# in your model file, e.g., ecommerce.model.lkml

connection: "your_database_connection"
include: "*.view.lkml" # Includes all view files in the directory

explore: orders {
  label: "Orders and Users"
  description: "Explore order data joined with user information."

  join: users {
    type: left_outer # Specifies the JOIN type
    relationship: many_to_one # Helps Looker optimize queries
    sql_on: ${orders.user_id} = ${users.id} ,, # The JOIN condition
  }
}

With this explore, a business user can now build a query that includes fields from both the orders and users tables, without ever thinking about how the join is executed.

Step 4: Connect the Looker Explore to Looker Studio

Once you've built and validated your model in Looker, it's time to bring that curated data into Looker Studio for visualization.

1. Open Looker Studio and create a new report.
2. In the "Add data to report" panel, search for the native <strong>Looker</strong> connector.
3. Authorize Looker Studio to access your Looker instance by providing its URL.
4. You will see a list of all the Models and Explores available to you.
5. Find and select the Orders and Users Explore we defined above.
6. Click "Add."

Your Looker Studio report now has a data source that is backed by your LookML model. In the "Data" panel on the right, you'll see all of the dimensions and measures you defined - Status, Created Date, Total Revenue, Order Count, etc., including fields from the joined users table. You can now drag and drop these fields to build charts, knowing the underlying logic is consistent, governed, and correct.

Actionable Tips for Building Your LookML Model

A well-structured model makes your Looker Studio reports significantly more user-friendly and powerful.

Use label and description Generously

The label parameter changes how a field name appears in the UI, and the description shows up when a user hovers over it. Use these to turn cryptic database column names like created_at into a friendly Order Date and provide context that helps your team understand what they are looking at.

Pre-Segment Data with a case Dimension

Don't force your users to write complex CASE statements in Looker Studio. Pre-build common segments directly in LookML. For example, you can create a dimension that categorizes orders based on their value.

dimension: order_value_tier {
  type: string
  case: {
    when: {
      sql: ${total_revenue} >= 1000 ,,
      label: "High Value"
    }
    when: {
      sql: ${total_revenue} < 1000 AND ${total_revenue} >= 250 ,,
      label: "Medium Value"
    }
    else: "Low Value"
  }
}

Now, Order Value Tier is a simple dimension that anyone can drag into their report to segment data instantly.

Ensure Consistent Formatting with value_format_name

Tired of manually formatting every metric as a currency or percentage in Looker Studio? Define it once in LookML.

For revenue, use value_format_name: usd or value_format_name: eur_0 to format the number as USD or Euros with no decimal places. For a conversion rate measure, use value_format_name: percent_2 to show it as a percentage with two decimal points. This formatting will automatically carry through to Looker Studio.

Final Thoughts

Mastering the relationship between Looker and Looker Studio is about understanding where to handle business logic. LookML is your engine room, used within Looker to forge a strong, reliable data model. Looker Studio is the cockpit, where you visualize the outputs from that powerful engine. By defining joins, calculations, and clear naming conventions in LookML, you empower your entire team to build insightful, consistent dashboards without needing a deep technical background.

Of course, building and maintaining a LookML model requires specialized knowledge and can become a bottleneck when your team needs answers fast. That’s where we wanted a simpler way forward. With Graphed, we connect your data sources in seconds and handle the complexities of data modeling for you. Instead of writing LookML, you use simple, natural language to create dashboards and reports. This gives you a similar outcome — fast, accurate insights from all your data — without the steep learning curve of a traditional BI tool.