# Connect a Databricks notebook to Tigris

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Connect a Databricks notebook to a Tigris bucket using serverless compute (the default in Databricks). Tigris is S3-compatible, so you can use `boto3` to list and read files stored in Tigris directly from your notebooks.

## Prerequisites

- Tigris **Access Key ID** and **Secret Access Key** (see the [Access Key guide](/content/docs/iam/manage-access-key/index.html) if you need to create one)
- Tigris **Endpoint**: `https://t3.storage.dev`
- A Tigris **bucket** with data to read

## 1. Create a notebook

Log in to your Databricks workspace and create a new notebook.

## 2. Install dependencies

```bash
pip install boto3 pandas pyarrow s3fs
```

Then restart the Python kernel:

```bash
%restart_python
```

## 3. Initialize the Tigris client

```python
import boto3

tigris_client = boto3.client(

's3',

aws_access_key_id='YOUR-ACCESS-KEY-ID',

aws_secret_access_key='YOUR-SECRET-ACCESS-KEY',

endpoint_url='https://t3.storage.dev',

region_name='auto'

)
```

Set `region_name` to `auto`. This works for all Tigris buckets.

## 4. Verify the connection

List your Tigris buckets to confirm the client is configured correctly:

```python
response = tigris_client.list_buckets()

print([bucket['Name'] for bucket in response['Buckets']])
```

## 5. Read a Parquet file

Download and read a Parquet file from your Tigris bucket:

```python
import pandas as pd

import pyarrow.parquet as pq

from io import BytesIO

bucket_name = 'databricks-test-bucket'

key = 'test/easy-00000-of-00002.parquet'

buffer = BytesIO()

tigris_client.download_fileobj(bucket_name, key, buffer)

buffer.seek(0)

table = pq.read_table(buffer)

df = table.to_pandas()

df.head()
```

You should see a preview of your Parquet file loaded into a Pandas DataFrame:

```plaintext
   column1   column2   column3

0  value_1  value_2  value_3

1  value_4  value_5  value_6

...
```