Deploy with Modal#

Introduction to Modal#

Modal is a serverless platform designed for developers. It allows you to run and deploy code in the cloud without managing infrastructure.

With Modal, you can perform tasks like running generative models, large-scale batch jobs, and job queues, all while easily scaling compute resources.

Modal features#

• Serverless Compute: No infrastructure management; scales automatically from zero to thousands of CPUs/GPUs.
• Cloud Functions: Run Python code in the cloud instantly and scale horizontally.
• GPU/CPU Scaling: Easily attach GPUs for heavy tasks like AI model training with a single line of code.
• Web Endpoints: Expose any function as an HTTPS API endpoint quickly.
• Scheduled Jobs: Convert Python functions into scheduled tasks effortlessly.

To learn more, please refer to Modal's documentation.

How to run dlt on Modal#

Here’s a dlt project setup to copy data from public MySQL database into DuckDB as a destination:

Step 1: Initialize source#

Run the dlt init CLI command to initialize the SQL database source and set up the sql_database_pipeline.py template.

dlt init sql_database duckdb

Step 2: Define Modal Image#

Open the file and define the Modal Image you want to run dlt in:

Step 3: Define Modal Function#

A Modal Function is a containerized environment that runs tasks.
It can be scheduled (e.g., daily or on a Cron schedule), request more CPU/memory, and scale across
multiple containers.

Here’s how to include your SQL pipeline in the Modal Function:

Step 4: Set up credentials#

You can securely store your credentials using Modal secrets. When you reference secrets within a Modal script,
the defined secret is automatically set as an environment variable. dlt natively supports environment variables,
enabling seamless integration of your credentials. For example, to declare a connection string, you can define it as follows:

SOURCES__SQL_DATABASE__CREDENTIALS=mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam

In the script above, the credentials specified are automatically utilized by dlt.
For more details, please refer to the documentation.

Step 5: Run pipeline#

Execute the pipeline once.
To run your pipeline a single time, use the following command:

modal run sql_pipeline.py

Step 6: Deploy#

If you want to deploy your pipeline on Modal for continuous execution or scheduling, use this command:

modal deploy sql_pipeline.py

Advanced configuration#

• Use Proxy IPs to connect to resources in your private network
• Sync tables in parallel using map()

More examples#

For a practical, real-world example, check out the article "Building a Cost-Effective Analytics Stack with Modal, dlt, and dbt".

This article illustrates how to automate a workflow for loading data from Postgres into Snowflake using dlt, providing valuable insights into building an efficient analytics pipeline.