# Run your first SQL query
**Time:** 5 minutes
**Prerequisites:** As a member of a SageMaker Unified Studio project, your IAM role needs the following managed policies:
+ [SageMakerStudioUserIAMConsolePolicy](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/security-iam-awsmanpol-SageMakerStudioUserIAMConsolePolicy.html) to sign in and access the project.
+ [SageMakerStudioUserIAMDefaultExecutionPolicy](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/adminguide/security-iam-awsmanpol-SageMakerStudioUserIAMDefaultExecutionPolicy.html) to access data and resources within the project.
If you don't have access, contact your administrator. If you are the administrator who set up the project, you already have the required permissions.
**Outcome:** You query sample data using the built-in query editor, see results inline, and understand how to browse your data catalog.
## What you will do
In this tutorial, you will:
+ Open the query editor in your project
+ Browse available tables in the data catalog
+ Write and run a SQL query on sample data
+ View and explore the results
SageMaker Unified Studio includes a built-in query editor that lets you write SQL queries against data stored in your lakehouse. The data can be in Amazon S3, Amazon Redshift, or other connected sources. You don't need to set up a separate query tool or configure credentials. Everything is already connected through your project.
## Step 1: Open the query editor
1. When you first sign in to SageMaker Unified Studio, you are in your default project. If you need to switch projects, use the project selector at the top of the page.
1. In the left navigation bar, choose **Query editor** under **Data analytics**.
![\[The SageMaker Unified Studio project overview page showing the Query editor option in the left navigation bar under Data analytics.\]](http://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/images/gs-sql/gs-sql-query-editor-nav.png)
This opens a *querybook*, an interactive SQL notebook where you can write multiple queries, add notes in markdown, and visualize results in one place.
![\[The querybook interface showing the Data explorer panel on the left with Catalogs, Connections, and Buckets, and an empty SQL cell on the right.\]](http://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/images/gs-sql/gs-sql-querybook.png)
**What is a querybook?**
A querybook is like a notebook for SQL. Each cell contains a SQL query or markdown text. You can run cells individually or all at once, and results appear inline below each query.
## Step 2: Browse your data
Before writing a query, review what data is available.
1. In the data explorer panel on the left side, expand **Catalogs**, then expand **AWSDataCatalog**.
1. Expand the **sagemaker\$1sample\$1db** database to see its tables. You should see a **churn** table.
1. Choose the **churn** table and review its columns and data types. This is sample data that was pre-configured in your project.
![\[The Data explorer panel showing the sagemaker_sample_db database expanded with the churn table and its columns including state, account_length, intl_plan, day_mins, and others.\]](http://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/images/gs-sql/gs-sql-data-explorer.png)
## Step 3: Run a SQL query
Now query the sample data. Copy the following SQL into a querybook cell:
```
SELECT
state,
COUNT(*) AS total_customers,
ROUND(AVG(day_mins), 2) AS avg_day_mins,
ROUND(AVG(eve_mins), 2) AS avg_eve_mins,
ROUND(AVG(custserv_calls), 2) AS avg_service_calls
FROM sagemaker_sample_db.churn
GROUP BY state
ORDER BY avg_service_calls DESC
LIMIT 10;
```
This query analyzes customer usage patterns by state. For each state, it calculates the total number of customers, their average daytime and evening minutes, and how often they contact customer service. The results show the top 10 states with the highest average service calls.
1. Paste the query into the SQL cell.
1. Choose **Athena (SQL)** from the engine selector dropdown at the top of the querybook.
1. Choose the **Run** button (▶) next to the cell.
1. Results appear in a table directly below the cell.
**Which query engine is running this?**
By default, your query runs on *Amazon Athena*, a serverless query engine that reads data directly from Amazon S3 without requiring you to load it into a database first. You can also switch to Amazon Redshift for data warehouse workloads using the engine selector in the querybook. You do not need to know the details of either engine to get started. Write standard SQL.
## Step 4: Explore your results
After your query runs, the results table shows each state with its usage metrics. You can:
+ **Sort columns** by choosing column headers.
+ **Download results** as a CSV file using the download button.
+ **Add another query** by choosing the **\$1** button to add a new SQL cell.
![\[Query results showing a table with columns for state, total_customers, avg_day_mins, avg_eve_mins, and avg_service_calls.\]](http://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/images/gs-sql/gs-sql-query-results.png)
Try a different query. For example, compare usage patterns for customers with and without an international plan:
```
SELECT
intl_plan,
COUNT(*) AS total_customers,
ROUND(AVG(intl_mins), 2) AS avg_intl_mins,
ROUND(AVG(intl_charge), 2) AS avg_intl_charge,
ROUND(AVG(custserv_calls), 2) AS avg_service_calls
FROM sagemaker_sample_db.churn
GROUP BY intl_plan;
```
This shows whether customers with an international plan use more international minutes and how their support call patterns compare.
![\[Query results showing a table comparing customers with and without an international plan, including total_customers, avg_intl_mins, avg_intl_charge, and avg_service_calls.\]](http://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/images/gs-sql/gs-sql-intl-plan-results.png)
## Ask in natural language
Instead of writing SQL yourself, you can ask the **Data Agent** to generate a query for you.
1. In the querybook, choose the **Chat with AI** icon in the top navigation bar. The Data Agent panel opens on the right side.
1. In the **Ask a question** text box at the bottom of the panel, type what you want in plain English, for example: *"Show me the top 5 states by average daytime charges"*
1. The Data Agent generates the SQL for you. Review it, then run it. The Data Agent has access to your project's data catalog, so it can identify which tables and columns are available.
![\[The querybook with the Data Agent chat panel open on the right side, showing suggested prompts for natural language queries.\]](http://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/images/gs-sql/gs-sql-data-agent.png)
After the Data Agent generates a query, choose **Accept** to insert it into a querybook cell, then choose **Run**.
![\[A generated SQL query inserted into a querybook cell, ready to run.\]](http://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/images/gs-sql/gs-sql-run-query.png)
The results appear in a table below the cell.
![\[Query results from the Data Agent-generated SQL, showing a table with the top states by average daytime charges.\]](http://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/images/gs-sql/gs-sql-first-query-results.png)
**SQL in notebooks**
You can also run SQL queries in a notebook using SQL cells. Notebooks let you combine SQL with Python code, visualizations, and markdown notes in a single document.
## What you learned
In this tutorial, you:
+ Opened the query editor and browsed available data in the catalog
+ Wrote and ran a SQL query to analyze customer usage by state
+ Explored results and ran a second query to compare international plan usage
+ Used the Data Agent to generate SQL from natural language