---
title: "Analyze your data in dbt"
source: dbt Core Documentation
source_url: https://github.com/dbt-labs/docs.getdbt.com
licence: Apache-2.0
domain: data-and-analysis
subdomain: dbt-docs
date_added: 2026-04-25
---

## Introduction

As a data analyst, you play a key role in transforming complex data into trusted, actionable insights for your team. With <Constant name="dbt" />, you can use built-in, AI-powered tools to build governed data models, explore how they’re built, and even run your own analysis.

In this quickstart, you’ll learn how to:

* Use <Constant name="catalog" /> to browse and understand data models across both dbt and Snowflake data assets
* Use <Constant name="insights" /> to run queries for exploring and validating your data
* Use <Constant name="canvas" /> to visually build your own data models
* Build confidence using <Constant name="dbt" /> as your workspace enhanced with AI

Here's more about the tools you will use on your journey:
* <Constant name="catalog" />: View your project's resources (such as models, tests, and metrics), their lineage, and query patterns to gain a better understanding of its latest production state.
* <Constant name="insights" />: Explore, validate, and query data with an intuitive, context-rich interface that bridges technical and business users by combining metadata, documentation, AI-assisted tools, and powerful querying capabilities.
* <Constant name="canvas" />: Quickly access and transform data through a visual, drag-and-drop experience and with a built-in AI for custom code generation.


## Prerequisites

Before you begin, make sure:

* You have access to and credentials configured for a <Constant name="dbt" /> project
* Your team has already run a successful dbt job, so models are built and ready
* You have a a git provider connected and authenticated

## Analyst workflows

Kimiko, an analyst at the Jaffle Shop, notices they've been doing a lot of new sales and wants to investigate the most critical data they have in their warehouse.

**Question: A stakeholder is curious how many customers you've acquired month by month, in the last 12 months.**

Kimiko wonders, "How do I find data in our project that will help me answer their question?"

### Explore a stakeholder question

She navigates to the data catalog, <Constant name="catalog" />, by signing into <Constant name="dbt" /> and clicking <Constant name="catalog" /> in the left panel. Because the question was about customers, Kimiko begins by searching for "customers" in <Constant name="catalog" />:

<Lightbox src="/img/guides/analyst-qs/catalog-search.png" width="90%" title="Catalog search for customers" />

She finds a "customers" model, which might be what she needs. She clicks **customers** to open the model. The description reads, “Customer overview data Mart offering key details for each unique customer, one row per customer.”

Next, Kimiko selects **Columns** to see which columns this model uses. 

<Lightbox src="/img/guides/analyst-qs/columns.png" width="90%" title="Columns in customers table" />

She notices these columns: `customer_ID`, `customer_names`, and `first_ordered_at`. 

The `first_ordered_at` column stands out to Kimiko, and she wonders if she might use it to see how many customers they've acquired based on when they placed their first order.

But first, she decides to interact with the data to learn more.

### Query data in Insights

From the **Customer model page** in <Constant name="catalog" />, Kimiko selects **Analyze data** from the **Open in...** dropdown. This enables her to query data for the Customer model. Once opened, <Constant name="insights" /> contains a query poised and ready to run.

<Lightbox src="/img/guides/analyst-qs/query.png" width="90%" title="Open query" />

When Kimiko runs the query, she can look at the data underyling it. The same context she saw in <Constant name="catalog" /> she now sees in her SQL editing experience.

As she looks through the data, she sees information about each customer. She also notices the `first_ordered_at` column. Kimiko wants to code the query but her SQL is a little rusty so she uses natural language in dbt Copilot:

*How many new customers did we get in each month last year? I'd like to use my customer model and the first ordered at field to do this analysis.*

dbt Copilot writes SQL that Kimiko decides to use: 

```sql
select 
    date_trunc('month', first_ordered_at) as month,
    count(customer_id) as new_customers
from {{ ref('customers') }}
where 
    date_part('year', first_ordered_at) = date_part('year', current_date) - 1
    and customer_type = 'new'
group by 1
order by 1;
```

Kimiko clicks **Replace** to move all of the SQL into her editor and replaces the original query. She runs the new query and reviews the data but decides to limit the dates using Copilot once again:

*Can we limit the dates to 2024?*

She verifies the new filter for 2024 and reruns this query: 

```sql
select 
    date_trunc('month', first_ordered_at) as month,
    count(customer_id) as new_customers
from {{ ref('customers') }}
where 
    date_part('year', first_ordered_at) = 2024
    and customer_type = 'new'
group by 1
order by 1;
```

She's happy with the results and clicks **Details** to see the AI-generated report, which includes a title and description, supplied SQL, and the compiled SQL.

<Lightbox src="/img/guides/analyst-qs/details.png" width="90%" title="Details report tabe" />

Once she's ready to get the insight to her stakeholder, she clicks **Chart** to view the chart prefilled with the data from the **Data** tab. 

She adds x- and y-axis labels, such as "Month of first order" and "Total new customers" to make it more comprehensible for the final report she'll share with her stakeholder. Next, she takes a screenshot to share with them.


She often comes back to this data so Kimiko decides to bookmark the page by clicking **Bookmark** in the top right. She also exports it to a CSV file.

### Visualize results

Kimiko has a few conversations with teammates and she finds out they're running pretty similar one-off queries, so she decides to take her long running query that she previously bookmarked and turn it into a full-fledged dbt model using <Constant name="canvas" />. She does this so she can share it with others, which de-duplicates work and helps her team become more efficient. 

To do this, she opens the query in <Constant name="insights" /> and clicks **Develop** then ***Develop in** <Constant name="canvas" />. This opens the SQL query in a visual form, represented in a DAG.


When she examines the model, she notes it's selecting from customers as expected, filtering for 2024, showing dates by month, and aggregating over that month. She runs it in her development environment and clicks **Commit** to submit a pull request. 


Now Kimiko's entire team, those who have the same access as her, can run this model and see the same results she does! What's more is they can help her improve the model as the stakeholder requests get more complicated, and she will benefit from their help.

### The query becomes a model

Going forward, Kimiko is able to return to her project in <Constant name="catalog" /> and run the model to get the most current results. From here, she can:

* Manually run the model, which also runs tests and is versioned so Kimiko can track changes over time
* Trigger a scheduled job to run the dbt model, like every Monday for her stakeholder report
* Set up a Slack notification in case the job fails so she can recitfy any problems
