On this page

DuckDB is an in-process embedded analytical database optimized for fast queries, ease of use, and embedding efficient analytics inside existing applications. It's the SQLite for analytics, but with a stronger focus on data analytics, aggregation, and Online Analytical Processing (OLAP) queries. Tigris dynamically distributes your data based on access patterns and handles lots of small files efficiently, enabling additional performance gains while using DuckDB.

If you want a full SQL lakehouse on top of Tigris — with tables, transactions, snapshots, and time travel rather than ad-hoc queries against files in a bucket — see the DuckLake guide. DuckLake stores data as Parquet files in your bucket and metadata in a separate SQL database, so you get an open lakehouse without the file-based-catalog complexity of Iceberg or Delta.

## Prerequisites

Make sure you have the following information from your Tigris account:

- Tigris **Access Key ID**
- Tigris **Secret Access Key**
- Tigris **Endpoint** (e.g., `https://t3.storage.dev`)
- Tigris **Bucket Name**

If you don't yet have access credentials, follow the steps in the Access Key guide to create one.

## Configure DuckDB to use Tigris

Open the DuckDB command line in your terminal:

```text
$ duckdb

v1.2.2 7c039464e4

Enter ".help" for usage hints.

Connected to a transient in-memory database.

Use ".open FILENAME" to reopen on a persistent database.

D
```

Create a new secret with the `CREATE SECRET` command:

```sql
CREATE OR REPLACE SECRET tigris

(      TYPE  s3

,  PROVIDER  config

,    KEY_ID  'tid_access_key_id'

,    SECRET  'tsec_secret_access_key'

,    REGION  'auto'

,  ENDPOINT  't3.storage.dev'

, URL_STYLE  'vhost'

);
```

## Query files in DuckDB

Once you're in, you can query files in your bucket like you would normally, just open them with the `s3://<bucketname>` prefix. For example, you can import the data from the LinkedIn Data Jobs Dataset by creating a local table like this:

```sql
CREATE TABLE IF NOT EXISTS jobs

( id               INT64  NOT NULL

, title            TEXT   NOT NULL

, company          TEXT   NOT NULL

, "location"       TEXT   NOT NULL

, link             TEXT   NOT NULL

, source           TEXT   NOT NULL

, date_posted      DATE   NOT NULL

, work_type        TEXT   NOT NULL

, employment_type  TEXT   NOT NULL

, "description"    TEXT   NOT NULL

);
```

And then importing the data from Tigris:

```sql
INSERT INTO jobs

SELECT * FROM

READ_CSV

( 's3://xe-duckdb/clean_jobs.csv'

, header = true

, columns =

{              'id': 'INT64'

,           'title': 'TEXT'

,         'company': 'TEXT'

,        'location': 'TEXT'

,            'link': 'TEXT'

,          'source': 'TEXT'

,     'date_posted': 'DATE'

,       'work_type': 'TEXT'

, 'employment_type': 'TEXT'

,     'description': 'TEXT'

}

);
```

Now you can query it like normal:

```sql
SELECT

*

FROM

jobs

WHERE

location LIKE 'Seattle%';
```