Looker: Unleashing the Power of Modern Business Intelligence

Looker is one of the most powerful Business Intelligence (BI) tools available today, trusted by organizations worldwide to transform raw data into actionable insights. Its robust capabilities empower teams to make data-driven decisions, streamline workflows, and unlock new growth opportunities.

This blog will explore some of Looker’s most widely used features, including custom dimensions and measures, aggregation and filtering, SQL Runner, dashboards, embedded analytics, alerting, and more. Whether you’re a seasoned analyst or just starting with BI tools, this guide will showcase how Looker can elevate your analytics game.

Let’s explore how Looker redefines the way businesses tackle their data.

1. Data Modeling with LookML

1.1 LookML

A flexible modeling language used to define the relationships and business logic in the data. To get a better understanding of LookML, you might check one of our previous blogs Overview of LookML Structures. If you want to dive deeper, you may check LookML terms and concepts.

1.2 Dimensions

In Looker, dimensions are unique attributes that help describe your data. Think of them as categories or details about your data. For example, in an “Airports” dataset, dimensions could include the city of the airport or its elevation.

Dimensions are used to group or filter data. They can represent:

• Attributes tied to a column in your database, like a product’s name or color.
• Facts or numerical values, such as product prices.
• Derived values, which are calculated using other data fields, like a product’s expiration date based on its creation date.

1.3 Customized Dimensions

Customized dimensions tailored to specific business needs. For instance, if your business tracks customer purchases, you could create a dimension like “Customer Type” (e.g., new vs. returning) to better analyze buying behavior.

1.4 Measures

In Looker, a measure is a calculated field that uses functions like COUNT, SUM, AVG (average), MIN, or MAX to analyze your data. Measures help you perform calculations on your data, like finding totals, averages, or percentages.

For example, in a sales dataset, measures might include:

• Total items sold (calculated using COUNT).
• Total sales revenue (calculated using SUM).
• Average sale price (calculated using AVG).

Measures work with grouped data to provide insights. For instance, you can calculate the total sales for each region or the average sale price by product category. The behavior of a measure depends on its type, such as numeric or time-based. To learn more about measure types, check the Measure types. By combining measures with dimensions, you can explore your data in powerful ways, track performance, and identify trends.

1.5 Pivots

In Looker, pivots help you organize and view your data more easily by rearranging it. When you pivot a dimension, its values are displayed horizontally as columns instead of rows. This makes the data easier to read and reduces the need to scroll down. For example, if you are analyzing sales data, you can pivot by “Month” to see each month’s sales as separate columns. This way, you can quickly compare data across months without losing context.

1.6 Looker Expressions and Table Calculations

In Looker, table calculations allow you to create custom metrics quickly and easily, similar to how you use formulas in tools like Excel. These calculations appear as green columns in your data table, while dimensions are blue and measures are orange.

Table calculations can be used to perform different types of calculations on your data, including:

• Mathematical calculations (like addition, subtraction, etc.)
• Logical calculations (true/false conditions)
• Lexical calculations (text-based, such as combining words)
• Date-based calculations (such as calculating the difference between two dates)

The formulas used in table calculations are known as Looker expressions.

Differences Between Table Calculations and Regular Fields

• Table calculations are easier to create and can be done by anyone in your organization, even if they don’t have development permissions or knowledge of LookML (Looker’s modeling language).
• Regular fields, on the other hand, are part of the data query itself and need development permissions to create.

Table calculations operate on the results from your query (the data you have already pulled), while regular fields are part of the query process. So, you will first select your dimensions and measures, run the report, and then apply table calculations to the results. To learn more about this feature you may check Creating Looker expressions and Using table calculations.

2. SQL Runner

SQL Runner in Looker gives you direct access to the underlying data tables in your database. This tool allows you to run custom SQL queries on your data, helping you retrieve exactly the information you need.

When you use SQL Runner, you will see a list of available data tables and columns. You can then write your own SQL queries to interact with the data in a more flexible way.

One of the useful features of SQL Runner is that you can save your custom SQL queries as derived tables. once you have saved a query as a derived table, you can perform further analysis, use it as a view, and visualize it just like you would with regular views.

SQL Runner is especially useful for advanced users who want to explore data beyond the standard reports or need custom calculations. It provides more control over how data is queried and displayed, making it a powerful tool for deeper data analysis. To get more information you may check SQL Runner basics.

3. Dashboards

3.1 Interactive Dashboards

Looker allows you to combine multiple visualizations and reports into one unified view. This enables you to explore different aspects of your data in a single place, and interact with the visualizations to drill down into specific insights, filter data, and customize the view based on your needs.

3.2 Filters

In Looker, filters help you refine and narrow down your data to focus on specific insights. There are different types of filters to suit various needs:

3.2.1 Global Filters

Global filters apply to the entire dashboard or report, affecting all visualizations and data in the view. For example, if you apply a global filter for “Date Range,” it will update all visualizations on the dashboard to reflect data within that selected time range. This is helpful when you want to apply the same filter across all data points at once.

3.2.2 Individual Filters

Individual filters are applied to specific visualizations or reports within a dashboard. This allows you to focus on filtering just one part of the data without affecting other visualizations. For instance, you might apply a filter for “Product Category” on one chart while keeping other charts unchanged. if you would like to learn more about the power of filtering in Looker, you may check Filtering and limiting data.

3.3 Drill-downs

It allows users to click on specific data points in a visualization (like a chart or table) to view more detailed information. This helps you explore your data step by step, zooming into the details behind the high-level numbers. Imagine you have a bar chart showing total sales by region. If you click on a specific region (e.g., “North America”), a drill-down will show you more detailed sales data for that region, such as sales by product, sales over time, or sales by store location. This lets you explore the data further without running multiple reports.

3.4 Embedded Dashboards

It allows you to integrate Looker dashboards directly into other applications or websites. This provides users with seamless access to data and reports without needing to leave the application they are already using. For example, if your company has an internal portal for employees, you can embed a Looker dashboard there. Employees can view real-time sales data, KPIs, and reports directly within the portal without needing to log into Looker separately. This makes it easy for everyone to access the information they need right within their existing workflows.

3.5 White-Labeling

White-Labeling in Looker allows you to fully customize the appearance of embedded content to match the look and feel of your own application or website. If you embed a Looker dashboard into your customer-facing website, you can customize the dashboard’s appearance to match your company’s branding. This includes changing the color scheme, fonts, and logo so the Looker dashboard feels like a natural part of your website rather than an external tool.

4. Delivery and Scheduling and Alerts

4.1 Scheduled Reports

It allows you to automate the delivery of reports and dashboards to users at regular intervals, such as daily, weekly, or monthly. This ensures that stakeholders always have the latest insights without needing to log into Looker.

4.2 Email Delivery

It lets you send reports and visualizations directly to recipients’ email inboxes. This feature is ideal for users who prefer to get updates via email instead of logging into the Looker platform.

4.3 Download Options

It lets you export reports and dashboards in various formats, including CSV, Excel, and PDF. This is useful for offline access, sharing, or further analysis in external tools.

4.4 Data Alerts

It helps you monitor metrics and send notifications when certain conditions are met. These alerts ensure you are immediately informed of important changes in your data. For example, you can set up an alert to notify you when the cost per click (CPC) for a marketing campaign exceeds a predefined threshold. This allows you to quickly pause or adjust the campaign to optimize spending and ensure better ROI.

4.5 Version History

Looker uses Git for version control, which allows you to track changes made to your data models and LookML code. This ensures you have a complete history of edits, enabling collaboration, rollbacks, and better management of your data development process. If a developer updates the logic for calculating profit margins, Git will show the previous version of the calculation and the updated version. This helps you understand what was changed and why. If a mistake is made or a change causes unexpected issues, you can quickly revert to a previous version of your LookML code. Git allows you to create branches, which are separate environments where you can test changes before applying them to the main project.

5. Exploration and Visualization

5.1 Explore Interface

It allows you to interact with your data dynamically. You can select dimensions, measures, and filters to build custom reports and visualizations without writing any SQL.

5.2 Visualization Types

Looker offers a variety of visualization types to help you present data in the most effective way. These include bar charts, line charts, pie charts, scatter plots, geo maps, and heatmaps. For more details, you may check Selecting an effective data visualization.

5.3 Cross-database Support

Looker allows you to work with data from multiple databases seamlessly. This means you can analyze and combine data stored in different systems without needing to move it all into one place. Imagine your company stores sales data in a Snowflake database and customer feedback in a BigQuery database. With Looker, you can create a report that combines both datasets, showing how customer satisfaction correlates with sales performance across different regions.

5.4 Looker Blocks

Looker offers pre-built templates that include data models, dashboards, and visualizations. They help you get started faster by providing a foundation that you can customize for your specific needs. For example, If you are setting up a marketing analytics dashboard, you can use a Looker Block designed for Google Ads or Facebook Ads. The block includes pre-built visualizations for ad performance, cost per click, ROI, etc. You can then customize it to align with your company’s goals.

5.5 Custom Visualizations

Beyond standard chart types, you can use HTML, inline CSS, and JavaScript to design unique, interactive components. This flexibility helps you craft dashboards that match your brand and enhance user experience.

5.5.1 Creating a Menu Bar to Link Dashboards

With custom visualizations, you can build a menu bar within a Looker dashboard that links to other dashboards. This is achieved using HTML and inline CSS.

Example

If you have separate dashboards for Sales Performance, Marketing Analytics, and Customer Insights, you can create a menu bar at the top of your main dashboard. Each menu item links to one of these dashboards, enabling seamless navigation.

Code Example for a Menu Bar

You can embed this code into a custom visualization:

<div style="display: flex; justify-content: space-around; background-color: #f4f4f4; padding: 10px;">

<a href="/dashboards/1" style="text-decoration: none; color: #007bff; font-weight: bold;">Sales Performance</a>

<a href="/dashboards/2" style="text-decoration: none; color: #007bff; font-weight: bold;">Marketing Analytics</a>

<a href="/dashboards/3" style="text-decoration: none; color: #007bff; font-weight: bold;">Customer Insights</a>

</div>

This creates a simple, clickable menu bar styled with inline CSS.

5.5.2. Adding Button Links

You can add buttons in a dashboard that link to external URLs and other dashboards or trigger actions. Buttons are designed using HTML and CSS for customization.

Example

A “View Details” button can link users to a specific report or dashboard or website or email.

Code Example for a Button

<div style="text-align: center; margin-top: 20px;">

<a href="/dashboards/4" style="padding: 10px 20px; background-color: #28a745; color: white; text-decoration: none; border-radius: 5px;">Questions?</a>

</div>

6. Data Governance and Security

6.1. Role-Based Access Control (RBAC)

RBAC allows you to assign roles to users or groups, defining what actions they can perform within Looker. These roles are linked to permissions and data access levels.

6.2 Data Permissions

It restricts access to specific datasets, ensuring users can only view data relevant to their role. This is often implemented using Row-Level Access Control or Data Filters.

For example;

• A regional manager for Europe should only see sales data for European countries. This can be enforced using row-level filters.
• A customer support agent might have access to customer feedback but not financial data.

How It Works

Use Looker’s Access Filters to apply restrictions dynamically. For instance, tie user attributes (like their region) to filter conditions in the data model.

6.3 Audit Logs

It provides a detailed history of user activity within Looker, allowing administrators to track actions, troubleshoot issues, and maintain security compliance.

Unlocking the Power of Data with Looker

Looker stands out as a powerful and versatile BI platform that transforms the way organizations interact with their data. From robust features like custom dimensions and measures, interactive dashboards, and pivots to advanced capabilities like custom visualizations, SQL Runner, and role-based access control, Looker empowers users to analyze, visualize, and share insights effectively.

With tools like scheduled reports and data alerts, Looker ensures critical information is delivered to the right people at the right time. Features such as cross-database support, Looker Blocks, and version control using Git further enhance collaboration, flexibility, and efficiency in data workflows.

Looker’s commitment to user-friendly design is evident in its Explore interface, intuitive filtering options, and the ability to create tailored solutions with embedded dashboards, white-labeling, and dynamic drill-downs. Combined with strong governance tools like RBAC, data permissions, and audit logs, Looker provides a secure and scalable platform for organizations to drive data-driven decision-making.

By leveraging Looker’s extensive feature set, businesses can unlock actionable insights, foster collaboration across teams, and build a data culture that fuels innovation and growth. It is not just a tool for data professionals—it is a bridge between technical and non-technical users, empowering everyone to harness the power of data.

With Looker, your data is not just numbers on a page—it is a story waiting to be told. Start telling yours today.