Quick Start

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What’s in this Quick Start?

This Quick Start guides you through the end-to-end Credible workflow, focusing on a familiar and powerful pattern: a data modeler defines trusted business logic in VS Code using our AI Copilot, and a data analyst explores the governed data model using Malloy’s no-code Explorer query builder. This mirrors the proven modeler → analyst workflow popularized by tools like DBT and Looker — now reimagined for the AI era. You will learn how to:

1. Build a semantic model using the Credible AI Copilot in VS Code
2. Create a view to validate your model
3. Publish your work to the Credible service for broader use
4. Explore your semantic model using the no-code Explorer to answer business questions

New to semantic models? Learn more about semantic models and why they matter →

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Step 0: Get Set Up

Before starting, make sure:

• A Credible admin set up your organization. (self-service demo coming soon)
• You have a basic understanding of Malloy, the semantic modeling language Credible is built on. View Malloy Docs →

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Install the VS Code Extension

1. In VS Code, go to the Extensions view (Cmd+Shift+X)
2. Search for Credible and install the extension

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Log in to Credible Service in VS Code

1. In the VS Code Explorer sidebar, expand “Credible Service.”
2. Click “Sign In”
3. Click ”+ Add new organization” and enter the organization name provided by your Credible admin
4. Complete the login flow in your browser by authenticating with you email or Google account
5. When redirected back to VS Code, select the project: quick-start

The Credible Service Panel in VS Code is where you can view your organization’s projects, connections, and packages in the Credible service. For example: In the quick-start project, you can see you have access to a “bq_demo” connection. The connection is managed by the Credible service and is not stored locally, but the Credible VS Code extension will discover it in your project and allow you to use it in your local models—thus eliminating the need to store database credentials locally.

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Step 1: Build a Semantic Model

At the core of Credible is the semantic model—a governed, versioned interface that defines how your data should be understood and used. Think of it as a an API for your data: it captures not just structure, but business meaning. You’ll use the Credible AI Copilot in VS Code to generate your first model using existing catalog metadata, query logs, and the structure of your data warehouse.

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Open the Credible Chat Panel

Open the Credible Chat Panel. You will initially see it on the left in Activity Bar. To open the AI Copilot chat, click on the Credible icon. To view the File Explorer, the Credible Service Panel and the Chat (along with your file), we recommend moving the chat over to the right panel by dragging and dropping the Credible Chat into a panel on the right, as shown in the screencast below:

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Generate Your Semantic Model

Open the Credible chat and ask the Credible AI Copilot to help you build a model on the ecommerce dataset. For example: Build a semantic model of our ecommerce data to analyze sales by brand or product and user spend by age bucket.

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Review and Adjust Your Model

After the model is generated, review and approve the edit to move the model into your malloy file. The ecommerce dataset has four tables: order_items, users, products, and inventory_items. You should see Malloy sources for each table with dimensions, measures, and join relationships connecting them logically. Understanding sources in Malloy: Sources define the underlying tables, join relationships, dimensions to slice by, and measures to compute aggregations on. Above each source definition, you’ll see three buttons: Schema (view table structure and joins), Explore (open the Data Explorer), and Preview (see the first 20 rows). Modifying the model with the Credible agent: To make adjustments, highlight any Malloy code block and ask the agent to update the code by describing the change you want in natural language (e.g., “make the age dimension an age range string such as 10-19”). For more information on Malloy syntax, see the Malloy documentation →

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Step 2: Create a View

Creating views is a powerful way to validate your semantic model and define reusable analyses. We’ll use the AI Copilot’s autocomplete feature to create a simple view that calculates sales by brand.

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Step-by-Step Instructions:

1. Find the order_items source in your ecommerce.malloy
2. Below the dimensions and measures, start typing view: sales_by_brand and pause (be sure to include a space after brand to trigger the copilot)
3. Wait for the copilot suggestion - it should suggest a complete view definition with a measure for total sales by brand. Adjust as desired & resolve any errors
4. Add a # bar_chart tag above the view to make it display as a bar chart
5. Click “Run” above the view execute the query. Results will display on the right

These steps are demonstrated in the following screencast:

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Create a Nested View

You can also create nested views to drill down into your data:

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Troubleshooting

If autocomplete isn’t working: add a space/tab after typing, wait 2-3 seconds, and ensure you start with valid Malloy syntax like view: .

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Step 3: Publish to the Credible Service

Publishing makes your model a governed, versioned asset—like a software package—that can be safely used by AI agents, applications, and dashboards. The version number is the key to managing changes over time.

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Voila!

Your semantic model is now:

• Governed and version controlled
• Discoverable and queryable via APIs and notebooks
• Ready for use by analysts, embedded apps, dashboards, or AI agents

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Step 4: Explore your Semantic Model

Now that you’ve published your semantic model to the Credible platform, it’s time to see how data consumers can analyze data in the no-code Explorer UI (built on Malloy’s open source Publisher & Explorer functionality). This is where your work as a data modeler becomes a launchpad for analysts, product managers, and other data consumers to ask meaningful questions — without writing a single line of SQL.

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Open the Data Explorer and Run a Saved View

1. Access your model: Navigate to https://<your-org>.admin.credibledata.com/quick-start, log in, and open your ecommerce package. Go to Models → ecommerce.malloy and select the order_items source tab.
2. Run an analysis: In the Views section, click on sales_by_brand, then click “Run” to execute the query.

✅ You should see the exact same result that you saw earlier in VS Code. For a complete guide to the Explorer interface, see the Data Explorer documentation →

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Create a Nested Query

Now let’s build a query from scratch using clicks: Clear the query panel, add brand as Group By and total_sales as Aggregate, then click + to add a nested query with user_age_group and total_sales. ✅ You’ve just created a nested query that breaks down sales by brand and age group.

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🎉 What You’ve Accomplished

✅ Defined business logic in a semantic model (with AI assistance)
✅ Validated the model and saved a view
✅ Published the package organization-wide
✅ Explored data with nested analysis through a no-code interface (no SQL required) You’ve experienced the power of Credible: one semantic model that works everywhere — from modeling to exploring, enabling developers and analysts to work together on the same trusted foundation.

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What’s Next?