Associate Data Practitioner

Create, modify, and share dashboards to answer business questions

Dashboards in Looker are powerful tools that bring multiple data visualizations together. A dashboard can include charts, tables, and metrics that help teams track key performance indicators. Understanding how to structure a dashboard is the first step to answering real business questions.

When you create a dashboard, you can add components like:

• Tiles that display individual visualizations
• Looks which are saved queries
• Text tiles for titles and descriptions

These elements work together to give a complete picture of the data.

To modify a dashboard, you can:

• Rearrange tiles by dragging them on the canvas
• Adjust filters to refine the data shown
• Change visualization types for clearer insights

These simple actions help keep dashboards up-to-date and relevant.

Sharing dashboards ensures that stakeholders get the insights they need. You can:

• Send a scheduled delivery via email
• Generate a shareable link with view or edit permissions
• Embed dashboards into other applications for easy access

Proper permissions maintain data security while enabling collaboration.

Finally, following best practices like consistent naming, clear labels, and organized layouts helps everyone quickly find and interpret the information they need.

Compare Looker and Looker Studio for different analytics use cases

Looker and Looker Studio are both Google Cloud tools for analytics, but they serve slightly different needs. Looker excels at governed data models and complex explorations. Looker Studio offers a user-friendly interface for quick, ad-hoc reporting.

When it comes to data modeling, Looker uses LookML, which provides:

• Centralized definitions of fields
• Reusable dimensions and measures
• Version control for changes

In contrast, Looker Studio connects directly to data sources without requiring a modeling layer, making it faster for simple setups.

On the visualization side, Looker Studio lets users drag and drop to build charts quickly. Looker supports more advanced options like:

• Custom Liquid templating
• Conditional formatting
• Detailed drill-down paths

These features make Looker a better choice for in-depth analysis.

Use cases vary: choose Looker for governed, repeatable analytics and deeper data exploration. Choose Looker Studio for lightweight dashboards, especially when you need to get insights up and running fast.

Manipulate simple LookML parameters to modify a data model

LookML is the modeling language that sits between raw data and visualizations in Looker. It lets you define how data is structured and labeled within the platform. Learning basic parameters helps you shape the data to meet business needs.

Key LookML components include views, explores, and fields. To adjust these, you use simple parameters such as:

• type: defines whether a field is a dimension or measure
• label: sets a friendly name for users
• sql: custom SQL for advanced calculations

These parameters let you tailor the data model precisely.

You can also use parameters to:

• Change data formats (e.g., date, number)
• Apply default filters to limit data scope
• Add descriptions for better user understanding

Each change improves clarity and usability for dashboard creators.

After updating LookML, you work in development mode to preview changes safely. Once satisfied, you commit updates through version control to share with the team. This process ensures that the data model remains consistent and reliable.

Conclusion

In this section, you learned how to create, modify, and share dashboards in Looker to answer real business questions. You explored the steps for building interactive dashboards, customizing visualizations, and using sharing features while maintaining data security.

You saw the key differences between Looker and Looker Studio, including when to use each tool based on data modeling needs and visualization complexity. Understanding these differences helps you choose the right platform for different analytics scenarios.

Finally, you examined how to manipulate simple LookML parameters to shape your data model. By defining fields, labels, and filters, you ensure that your data is organized, consistent, and ready for analysis. Mastering these foundational skills sets you up to deliver clear insights and effective dashboards in Looker.