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:

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:

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

This opens a querybook, an interactive SQL notebook where you can write multiple queries, add notes in markdown, and visualize results in one place.

Step 2: Browse your data

Before writing a query, review what data is available.

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.

Step 4: Explore your results

After your query runs, the results table shows each state with its usage metrics. You can:

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.

Ask in natural language

Instead of writing SQL yourself, you can ask the Data Agent to generate a query for you.

After the Data Agent generates a query, choose Accept to insert it into a querybook cell, then choose Run.

The results appear in a table below the cell.

What you learned

In this tutorial, you: