---
sidebar_position: 1
title: "1 - Running Your First Query"
sidebar_label: "1 - First Query"
description: "Learn MotherDuck and DuckDB by running your first queries on shared data"
sql_editor: true
---

import MotherDuckSQLEditor from '@site/src/components/MotherDuckSQLEditor';
import Versions from '@site/src/components/Versions';

In this multi-part tutorial, you will go through a full end-to-end example on how to use MotherDuck and DuckDB, **push** and **share** data, take advantage of **hybrid query** execution and query data using SQL through the **MotherDuck UI** or **DuckDB CLI**.

:::note
MotherDuck supports DuckDB <Versions region="us-east-1" bound="max" />. In **US East (N. Virginia) -** `us-east-1`, MotherDuck is compatible with client versions <Versions region="us-east-1" bound="min" /> through <Versions region="us-east-1" bound="max" />. In **Europe (Frankfurt) -** `eu-central-1`, MotherDuck supports client versions <Versions region="eu-central-1" bound="min" /> through <Versions region="eu-central-1" bound="max" />.
:::

## Running your first query

### Query from a shared database

Before playing with the dataset we just downloaded, let's run a couple simple queries on the shared sample database. This database contains a series of MotherDuck's public datasets and it's *auto-attached* for each user, meaning it's accessible directly within your MotherDuck session without any additional setup.

We will query the NYC 311 dataset first. This dataset contains over thirty million complaints citizens have filed with the New York City government. We'll select several columns and look at the complaints filed over a few days to demonstrate the [Column Explorer](https://motherduck.com/blog/introducing-column-explorer/) feature of the MotherDuck UI.

<MotherDuckSQLEditor
  database="sample_data"
  query="SELECT created_date, agency_name, complaint_type, descriptor, incident_address, resolution_description FROM sample_data.nyc.service_requests WHERE created_date >= '2022-03-27' AND created_date <= '2022-03-31';" />


Want to explore the full interface? Try running this query in the [MotherDuck UI](https://app.motherduck.com/) to experience the complete dashboard, visual query builder, and advanced analytics features.

:::info
In the MotherDuck UI, the Column Explorer provides quick visual summaries of your data, helping you understand distributions and patterns at a glance.
![Column Explorer showing data distribution summaries in the MotherDuck UI](./img/demo_ui_column_explorer.png)
:::

For the remainder of this tutorial, we'll focus on the NYC taxi data and perform aggregation queries representative of the types of queries often performed in analytics databases.  We will first get the average fare based on the number of passengers. The source dataset covers data for the whole month of November 2022.

<MotherDuckSQLEditor
  database="sample_data"
  query="SELECT passenger_count, avg(total_amount) FROM sample_data.nyc.taxi GROUP BY passenger_count ORDER by passenger_count;" />

:::info
The `sample_data` database is auto-attached but for any other shared database you would like to read, you would need to use the `ATTACH` statement. Read more about [querying shared MotherDuck databases](/key-tasks/sharing-data/sharing-data.mdx).
:::

:::tip
**Using a DuckDB client?** You can run these same queries in any of the DuckDB client after connecting with `ATTACH 'md:';` - you'll be prompted to authenticate if no `motherduck_token` is found as environment variable.
:::

### Query from S3
Our shared sample database is great to play with but you probably want to use your own data on AWS S3. Let's see how to do that.

The sample database source data is actually available on our public AWS S3 bucket. Let's run the exact same query but instead of pointing to a MotherDuck table, we will point to a parquet file on S3.

For a secured bucket, we need to pass the AWS credentials - check [authenticating to S3](../../integrations/cloud-storage/amazon-s3.mdx) for more information.

Here's the updated query while reading from S3:

<MotherDuckSQLEditor
  database="sample_data"
  query="SELECT passenger_count, avg(total_amount) FROM 's3://us-prd-motherduck-open-datasets/nyc_taxi/parquet/yellow_cab_nyc_2022_11.parquet' GROUP BY passenger_count ORDER by passenger_count;" />

:::info
DuckDB automatically detects the appropriate reader based on file extension, so there’s no need to explicitly specify a function. However, if you need more control over how files are read, you can use the corresponding functions directly:

```sql
SELECT * FROM read_parquet('my_data.parquet');
SELECT * FROM read_csv_auto('my_data.csv');
SELECT * FROM read_json_auto('my_data.json');
```

These functions allow you to customize parsing behavior or override automatic detection when needed.
:::

## Next steps

Great! You've successfully run your first queries on MotherDuck. You've learned how to:

✅ Query shared databases like `sample_data`  
✅ Read data directly from S3  

👉 **[Continue to Part 2: Loading Your Dataset →](../part-2)**