diff --git a/Makefile b/Makefile
index 4a786ed528..faf970708e 100644
--- a/Makefile
+++ b/Makefile
@@ -65,6 +65,7 @@ lint-and-test-snippets:
 	poetry run mypy --config-file mypy.ini docs/website docs/examples docs/tools --exclude docs/tools/lint_setup --exclude docs/website/docs_processed
 	poetry run flake8 --max-line-length=200 docs/website docs/examples docs/tools
 	cd docs/website/docs && poetry run pytest --ignore=node_modules
+	modal run docs/website/docs/walkthroughs/deploy_a_pipeline/deploy-with-modal-snippets.py
 
 lint-and-test-examples:
 	cd docs/tools && poetry run python prepare_examples_tests.py
@@ -72,7 +73,6 @@ lint-and-test-examples:
 	poetry run mypy --config-file mypy.ini docs/examples
 	cd docs/examples && poetry run pytest
 
-
 test-examples:
 	cd docs/examples && poetry run pytest
 
@@ -107,7 +107,7 @@ test-build-images: build-library
 	docker build -f deploy/dlt/Dockerfile.airflow --build-arg=COMMIT_SHA="$(shell git log -1 --pretty=%h)" --build-arg=IMAGE_VERSION="$(shell poetry version -s)" .
 	# docker build -f deploy/dlt/Dockerfile --build-arg=COMMIT_SHA="$(shell git log -1 --pretty=%h)" --build-arg=IMAGE_VERSION="$(shell poetry version -s)" .
 
-preprocess-docs: 
+preprocess-docs:
 	# run docs preprocessing to run a few checks and ensure examples can be parsed
 	cd docs/website && npm i && npm run preprocess-docs
 
diff --git a/deploy/dlt/README.md b/deploy/dlt/README.md
index 010a2d0f67..88b8f71a79 100644
--- a/deploy/dlt/README.md
+++ b/deploy/dlt/README.md
@@ -1 +1 @@
-Example `Dockerfile` that installs `dlt` package on an alpine linux image. For actual pipeline deployment please refer to [deploy a pipeline walkthrough](https://dlthub.com/docs/walkthroughs/deploy-a-pipeline/deploy-with-github-actions)
+Example `Dockerfile` that installs `dlt` package on an alpine linux image. For actual pipeline deployment please refer to [deploy a pipeline walkthrough](https://dlthub.com/docs/walkthroughs/deploy_a_pipeline/deploy-with-github-actions)
diff --git a/dlt/cli/deploy_command.py b/dlt/cli/deploy_command.py
index 88c132f5e2..36895c923c 100644
--- a/dlt/cli/deploy_command.py
+++ b/dlt/cli/deploy_command.py
@@ -26,9 +26,9 @@
 from dlt.common.destination.reference import Destination
 
 REQUIREMENTS_GITHUB_ACTION = "requirements_github_action.txt"
-DLT_DEPLOY_DOCS_URL = "https://dlthub.com/docs/walkthroughs/deploy-a-pipeline"
+DLT_DEPLOY_DOCS_URL = "https://dlthub.com/docs/walkthroughs/deploy_a_pipeline"
 DLT_AIRFLOW_GCP_DOCS_URL = (
-    "https://dlthub.com/docs/walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer"
+    "https://dlthub.com/docs/walkthroughs/deploy_a_pipeline/deploy-with-airflow-composer"
 )
 AIRFLOW_GETTING_STARTED = "https://airflow.apache.org/docs/apache-airflow/stable/start.html"
 AIRFLOW_DAG_TEMPLATE_SCRIPT = "dag_template.py"
diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/usage.md b/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/usage.md
index bdc440630d..d216fbd1f4 100644
--- a/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/usage.md
+++ b/docs/website/docs/dlt-ecosystem/verified-sources/sql_database/usage.md
@@ -98,11 +98,11 @@ Examples:
 
 ## Deploying the sql_database pipeline
 
-You can deploy the `sql_database` pipeline with any of the `dlt` deployment methods, such as [GitHub Actions](../../../walkthroughs/deploy-a-pipeline/deploy-with-github-actions), [Airflow](../../../walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer), [Dagster](../../../walkthroughs/deploy-a-pipeline/deploy-with-dagster), etc. See [here](../../../walkthroughs/deploy-a-pipeline) for a full list of deployment methods.
+You can deploy the `sql_database` pipeline with any of the `dlt` deployment methods, such as [GitHub Actions](../../../walkthroughs/deploy_a_pipeline/deploy-with-github-actions), [Airflow](../../../walkthroughs/deploy_a_pipeline/deploy-with-airflow-composer), [Dagster](../../../walkthroughs/deploy_a_pipeline/deploy-with-dagster), etc. See [here](../../../walkthroughs/deploy_a_pipeline) for a full list of deployment methods.
 
 ### Running on Airflow
 When running on Airflow:
-1. Use the `dlt` [Airflow Helper](../../../walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer.md#2-modify-dag-file) to create tasks from the `sql_database` source. (If you want to run table extraction in parallel, you can do this by setting `decompose = "parallel-isolated"` when doing the source->DAG conversion. See [here](../../../walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer#2-modify-dag-file) for a code example.)
+1. Use the `dlt` [Airflow Helper](../../../walkthroughs/deploy_a_pipeline/deploy-with-airflow-composer.md#2-modify-dag-file) to create tasks from the `sql_database` source. (If you want to run table extraction in parallel, you can do this by setting `decompose = "parallel-isolated"` when doing the source->DAG conversion. See [here](../../../walkthroughs/deploy_a_pipeline/deploy-with-airflow-composer#2-modify-dag-file) for a code example.)
 2. Reflect tables at runtime with the `defer_table_reflect` argument.
 3. Set `allow_external_schedulers` to load data using [Airflow intervals](../../../general-usage/incremental-loading.md#using-airflow-schedule-for-backfill-and-incremental-loading).
 
diff --git a/docs/website/docs/general-usage/incremental-loading.md b/docs/website/docs/general-usage/incremental-loading.md
index c8f92cf154..598b75be59 100644
--- a/docs/website/docs/general-usage/incremental-loading.md
+++ b/docs/website/docs/general-usage/incremental-loading.md
@@ -854,7 +854,7 @@ Here we call **get_resource(endpoint)** and that creates an un-evaluated generat
 
 ### Using Airflow schedule for backfill and incremental loading
 
-When [running an Airflow task](../walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer.md#2-modify-dag-file), you can opt-in your resource to get the `initial_value`/`start_value` and `end_value` from the Airflow schedule associated with your DAG. Let's assume that the **Zendesk tickets** resource contains a year of data with thousands of tickets. We want to backfill the last year of data week by week and then continue with incremental loading daily.
+When [running an Airflow task](../walkthroughs/deploy_a_pipeline/deploy-with-airflow-composer.md#2-modify-dag-file), you can opt-in your resource to get the `initial_value`/`start_value` and `end_value` from the Airflow schedule associated with your DAG. Let's assume that the **Zendesk tickets** resource contains a year of data with thousands of tickets. We want to backfill the last year of data week by week and then continue with incremental loading daily.
 
 ```py
 @dlt.resource(primary_key="id")
diff --git a/docs/website/docs/general-usage/source.md b/docs/website/docs/general-usage/source.md
index f91eca58de..5cc342f28e 100644
--- a/docs/website/docs/general-usage/source.md
+++ b/docs/website/docs/general-usage/source.md
@@ -50,7 +50,7 @@ used when loading the source.
 
 Do not extract data in the source function. Leave that task to your resources if possible. The source function is executed immediately when called (contrary to resources which delay execution - like Python generators). There are several benefits (error handling, execution metrics, parallelization) you get when you extract data in `pipeline.run` or `pipeline.extract`.
 
-If this is impractical (for example, you want to reflect a database to create resources for tables), make sure you do not call the source function too often. [See this note if you plan to deploy on Airflow](../walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer.md#2-modify-dag-file)
+If this is impractical (for example, you want to reflect a database to create resources for tables), make sure you do not call the source function too often. [See this note if you plan to deploy on Airflow](../walkthroughs/deploy_a_pipeline/deploy-with-airflow-composer.md#2-modify-dag-file)
 
 
 ## Customize sources
diff --git a/docs/website/docs/intro.md b/docs/website/docs/intro.md
index 650c47920b..a7200f49d4 100644
--- a/docs/website/docs/intro.md
+++ b/docs/website/docs/intro.md
@@ -18,7 +18,7 @@ dlt is designed to be easy to use, flexible, and scalable:
 
 - dlt infers [schemas](./general-usage/schema) and [data types](./general-usage/schema/#data-types), [normalizes the data](./general-usage/schema/#data-normalizer), and handles nested data structures.
 - dlt supports a variety of [popular destinations](./dlt-ecosystem/destinations/) and has an interface to add [custom destinations](./dlt-ecosystem/destinations/destination) to create reverse ETL pipelines.
-- dlt can be deployed anywhere Python runs, be it on [Airflow](./walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer), [serverless functions](./walkthroughs/deploy-a-pipeline/deploy-with-google-cloud-functions), or any other cloud deployment of your choice.
+- dlt can be deployed anywhere Python runs, be it on [Airflow](./walkthroughs/deploy_a_pipeline/deploy-with-airflow-composer), [serverless functions](./walkthroughs/deploy_a_pipeline/deploy-with-google-cloud-functions), or any other cloud deployment of your choice.
 - dlt automates pipeline maintenance with [schema evolution](./general-usage/schema-evolution) and [schema and data contracts](./general-usage/schema-contracts).
 
 To get started with dlt, install the library using pip:
diff --git a/docs/website/docs/reference/command-line-interface.md b/docs/website/docs/reference/command-line-interface.md
index e29b43bcba..6c8c12670e 100644
--- a/docs/website/docs/reference/command-line-interface.md
+++ b/docs/website/docs/reference/command-line-interface.md
@@ -56,7 +56,7 @@ schedule into quotation marks as in the example above.
 For the chess.com API example above, you could deploy it with
 `dlt deploy chess.py github-action --schedule "*/30 * * * *"`.
 
-Follow the guide on [how to deploy a pipeline with GitHub Actions](../walkthroughs/deploy-a-pipeline/deploy-with-github-actions) to learn more.
+Follow the guide on [how to deploy a pipeline with GitHub Actions](../walkthroughs/deploy_a_pipeline/deploy-with-github-actions) to learn more.
 
 ### `airflow-composer`
 
@@ -66,7 +66,7 @@ dlt deploy <script>.py airflow-composer
 
 [Google Composer](https://cloud.google.com/composer?hl=en) is a managed Airflow environment provided by Google.
 
-Follow the guide on [how to deploy a pipeline with Airflow](../walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer) to learn more.
+Follow the guide on [how to deploy a pipeline with Airflow](../walkthroughs/deploy_a_pipeline/deploy-with-airflow-composer) to learn more.
 
 It will create an Airflow DAG for your pipeline script that you should customize. The DAG is using
 `dlt` [Airflow wrapper](https://github.com/dlt-hub/dlt/blob/devel/dlt/helpers/airflow_helper.py#L37) to make this process trivial.
diff --git a/docs/website/docs/reference/explainers/airflow-gcp-cloud-composer.md b/docs/website/docs/reference/explainers/airflow-gcp-cloud-composer.md
index a6243f803f..c142b42d65 100644
--- a/docs/website/docs/reference/explainers/airflow-gcp-cloud-composer.md
+++ b/docs/website/docs/reference/explainers/airflow-gcp-cloud-composer.md
@@ -10,7 +10,7 @@ keywords: [airflow, github, google cloud composer]
 
 This setup will allow you to deploy the main branch of your Airflow project from GitHub to Cloud Composer.
 
-- Create a GitHub repository, for example, by following our how-to guide on [deployment for Airflow](../../walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer.md).
+- Create a GitHub repository, for example, by following our how-to guide on [deployment for Airflow](../../walkthroughs/deploy_a_pipeline/deploy-with-airflow-composer.md).
 
 - In the Google Cloud web interface, go to Source Repositories and create a repository that mirrors your GitHub repository. This will simplify authentication by using this mirroring service.
 
diff --git a/docs/website/docs/running-in-production/monitoring.md b/docs/website/docs/running-in-production/monitoring.md
index ec16a18524..ed898ad5ef 100644
--- a/docs/website/docs/running-in-production/monitoring.md
+++ b/docs/website/docs/running-in-production/monitoring.md
@@ -9,7 +9,7 @@ keywords: [monitoring, run monitoring, data monitoring, airflow, github actions]
 Monitoring and [alerting](alerting.md) are used together to provide a more complete picture of the
 health of a data product. With monitoring, we examine much more information than we consider when
 alerting. Monitoring is meant to give a fast, simple overview of the health of the system. How to
-best monitor a `dlt` pipeline will depend on your [deployment method](../walkthroughs/deploy-a-pipeline/).
+best monitor a `dlt` pipeline will depend on your [deployment method](../walkthroughs/deploy_a_pipeline/).
 
 ## Run monitoring
 
diff --git a/docs/website/docs/tutorial/load-data-from-an-api.md b/docs/website/docs/tutorial/load-data-from-an-api.md
index 3640f0e8d7..1385767baa 100644
--- a/docs/website/docs/tutorial/load-data-from-an-api.md
+++ b/docs/website/docs/tutorial/load-data-from-an-api.md
@@ -561,7 +561,7 @@ That's it! Now you have a reusable source that can load data from any GitHub rep
 Congratulations on completing the tutorial! You've come a long way since the [getting started](../intro) guide. By now, you've mastered loading data from various GitHub API endpoints, organizing resources into sources, managing secrets securely, and creating reusable sources. You can use these skills to build your own pipelines and load data from any source.
 
 Interested in learning more? Here are some suggestions:
-1. You've been running your pipelines locally. Learn how to [deploy and run them in the cloud](../walkthroughs/deploy-a-pipeline/).
+1. You've been running your pipelines locally. Learn how to [deploy and run them in the cloud](../walkthroughs/deploy_a_pipeline/).
 2. Dive deeper into how dlt works by reading the [Using dlt](../general-usage) section. Some highlights:
     - [Set up "last value" incremental loading](../general-usage/incremental-loading#incremental_loading-with-last-value).
     - Learn about data loading strategies: [append, replace, and merge](../general-usage/incremental-loading).
diff --git a/docs/website/docs/walkthroughs/__init__.py b/docs/website/docs/walkthroughs/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/docs/website/docs/walkthroughs/create-a-pipeline.md b/docs/website/docs/walkthroughs/create-a-pipeline.md
index d13b654654..1698d3a204 100644
--- a/docs/website/docs/walkthroughs/create-a-pipeline.md
+++ b/docs/website/docs/walkthroughs/create-a-pipeline.md
@@ -162,7 +162,7 @@ This will open a Streamlit app that gives you an overview of the data loaded.
 With a functioning pipeline, consider exploring:
 
 - Our [REST Client](../general-usage/http/rest-client).
-- [Deploy this pipeline with GitHub Actions](deploy-a-pipeline/deploy-with-github-actions), so that the data is automatically loaded on a schedule.
+- [Deploy this pipeline with GitHub Actions](deploy_a_pipeline/deploy-with-github-actions), so that the data is automatically loaded on a schedule.
 - Transform the [loaded data](../dlt-ecosystem/transformations) with dbt or in Pandas DataFrames.
 - Learn how to [run](../running-in-production/running), [monitor](../running-in-production/monitoring), and [alert](../running-in-production/alerting) when you put your pipeline in production.
 - Try loading data to a different destination like [Google BigQuery](../dlt-ecosystem/destinations/bigquery), [Amazon Redshift](../dlt-ecosystem/destinations/redshift), or [Postgres](../dlt-ecosystem/destinations/postgres).
diff --git a/docs/website/docs/walkthroughs/deploy_a_pipeline/__init__.py b/docs/website/docs/walkthroughs/deploy_a_pipeline/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-gcp-cloud-function-as-webhook.md b/docs/website/docs/walkthroughs/deploy_a_pipeline/deploy-gcp-cloud-function-as-webhook.md
similarity index 100%
rename from docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-gcp-cloud-function-as-webhook.md
rename to docs/website/docs/walkthroughs/deploy_a_pipeline/deploy-gcp-cloud-function-as-webhook.md
diff --git a/docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer.md b/docs/website/docs/walkthroughs/deploy_a_pipeline/deploy-with-airflow-composer.md
similarity index 100%
rename from docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer.md
rename to docs/website/docs/walkthroughs/deploy_a_pipeline/deploy-with-airflow-composer.md
diff --git a/docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-dagster.md b/docs/website/docs/walkthroughs/deploy_a_pipeline/deploy-with-dagster.md
similarity index 100%
rename from docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-dagster.md
rename to docs/website/docs/walkthroughs/deploy_a_pipeline/deploy-with-dagster.md
diff --git a/docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-github-actions.md b/docs/website/docs/walkthroughs/deploy_a_pipeline/deploy-with-github-actions.md
similarity index 100%
rename from docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-github-actions.md
rename to docs/website/docs/walkthroughs/deploy_a_pipeline/deploy-with-github-actions.md
diff --git a/docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-google-cloud-functions.md b/docs/website/docs/walkthroughs/deploy_a_pipeline/deploy-with-google-cloud-functions.md
similarity index 100%
rename from docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-google-cloud-functions.md
rename to docs/website/docs/walkthroughs/deploy_a_pipeline/deploy-with-google-cloud-functions.md
diff --git a/docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-kestra.md b/docs/website/docs/walkthroughs/deploy_a_pipeline/deploy-with-kestra.md
similarity index 100%
rename from docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-kestra.md
rename to docs/website/docs/walkthroughs/deploy_a_pipeline/deploy-with-kestra.md
diff --git a/docs/website/docs/walkthroughs/deploy_a_pipeline/deploy-with-modal-snippets.py b/docs/website/docs/walkthroughs/deploy_a_pipeline/deploy-with-modal-snippets.py
new file mode 100644
index 0000000000..0df22ec160
--- /dev/null
+++ b/docs/website/docs/walkthroughs/deploy_a_pipeline/deploy-with-modal-snippets.py
@@ -0,0 +1,57 @@
+import os
+from tests.pipeline.utils import assert_load_info
+import modal
+
+# @@@DLT_SNIPPET_START modal_image
+# Define the Modal Image
+image = modal.Image.debian_slim().pip_install(
+    "dlt>=1.1.0",
+    "dlt[duckdb]",  # destination
+    "dlt[sql_database]",  # source (postgres)
+    "pymysql",  # database driver for MySQL source
+)
+
+app = modal.App("example-dlt", image=image)
+
+# Modal Volume used to store the duckdb database file
+vol = modal.Volume.from_name("duckdb-vol", create_if_missing=True)
+# @@@DLT_SNIPPET_END modal_image
+
+# @@@DLT_SNIPPET_START modal_function
+@app.function(
+    volumes={"/data/": vol},
+    schedule=modal.Period(days=1),
+    secrets=[modal.Secret.from_name("sql-secret")],
+)
+def load_tables():
+    import dlt
+    from dlt.sources.sql_database import sql_database
+
+    # Define the source database credentials; in production, you would save this as a Modal Secret which can be referenced here as an environment variable
+    os.environ['SOURCES__SQL_DATABASE__CREDENTIALS']="mysql+pymysql://rfamro@mysql-rfam-public.ebi.ac.uk:4497/Rfam"
+
+    # Load tables "family" and "genome"
+    source = sql_database().with_resources("family", "genome")
+
+    # Create dlt pipeline object
+    pipeline = dlt.pipeline(
+        pipeline_name="sql_to_duckdb_pipeline",
+        destination=dlt.destinations.duckdb(
+            "/data/rfam.duckdb"
+        ),  # write the duckdb database file to this file location, which will get mounted to the Modal Volume
+        dataset_name="sql_to_duckdb_pipeline_data",
+        progress="log",  # output progress of the pipeline
+    )
+
+    # Run the pipeline
+    load_info = pipeline.run(source)
+
+    # Print run statistics
+    print(load_info)
+    # @@@DLT_SNIPPET_END modal_function
+
+    assert_load_info(load_info)
+
+def modal_snippet() -> None:
+    # Any additional logic or calling the function
+    pass
diff --git a/docs/website/docs/walkthroughs/deploy_a_pipeline/deploy-with-modal.md b/docs/website/docs/walkthroughs/deploy_a_pipeline/deploy-with-modal.md
new file mode 100644
index 0000000000..d650e0e1b7
--- /dev/null
+++ b/docs/website/docs/walkthroughs/deploy_a_pipeline/deploy-with-modal.md
@@ -0,0 +1,111 @@
+---
+title: Deploy with Modal
+description: How to deploy a pipeline with Modal
+keywords: [how to, deploy a pipeline, Modal]
+canonical: https://modal.com/blog/analytics-stack
+---
+
+# Deploy with Modal
+
+## Introduction to Modal
+
+[Modal](https://modal.com/) 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 know more, please refer to [Modals's documentation.](https://modal.com/docs)
+
+## Building data pipelines with dlt
+
+dlt is an open-source Python library that allows you to declaratively load data sources into well-structured tables or datasets. It does this through automatic schema inference and evolution. The library simplifies building data pipelines by providing functionality to support the entire extract and load process.
+
+### How does dlt integrate with Modal for pipeline orchestration?
+
+As an example of how to set up a pipeline in Modal, we'll use the [building a cost-effective analytics stack with Modal, dlt, and dbt.](https://modal.com/blog/analytics-stack) case study.
+
+The example demonstrates automating a workflow to load data from Postgres to Snowflake using dlt.
+
+## How to run dlt on Modal
+
+Here’s a dlt project setup to copy data from our MySQL into DuckDB:  
+
+1. Run the `dlt init` CLI command to initialize the SQL database source and set up the `sql_database_pipeline.py` template.
+   ```sh
+   dlt init sql_database duckdb
+   ```
+2. Open the file and define the Modal Image you want to run `dlt` in:
+   <!--@@@DLT_SNIPPET modal_image-->
+
+3. Define a 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:
+   
+   <!--@@@DLT_SNIPPET modal_function-->
+   
+4. 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:
+   ```text
+   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.](../../general-usage/credentials/setup#environment-variables)
+   
+4. Execute the pipeline once:
+   To run your pipeline a single time, use the following command:
+   ```sh
+   modal run sql_pipeline.py
+   ```
+
+5. Deploy the pipeline 
+   If you want to deploy your pipeline on Modal for continuous execution or scheduling, use this command:   
+   ```sh
+   modal deploy sql_pipeline.py
+   ```
+
+## Advanced configuration
+### Modal Proxy
+
+If your database is in a private VPN, you can use [Modal Proxy](https://modal.com/docs/reference/modal.Proxy) as a bastion server (available for Enterprise customers). 
+To connect to a production read replica, attach the proxy to the function definition and change the hostname to localhost:
+```py
+@app.function(
+    secrets=[
+        modal.Secret.from_name("snowflake-secret"),
+        modal.Secret.from_name("postgres-read-replica-prod"),
+    ],
+    schedule=modal.Cron("24 6 * * *"),
+    proxy=modal.Proxy.from_name("prod-postgres-proxy", environment_name="main"),
+    timeout=3000,
+)
+def task_pipeline(dev: bool = False) -> None:
+    pg_url = f'postgresql://{os.environ["PGUSER"]}:{os.environ["PGPASSWORD"]}@localhost:{os.environ["PGPORT"]}/{os.environ["PGDATABASE"]}'
+```
+
+### Capturing deletes
+To capture updates or deleted rows from your database, consider using dlt's [Postgres CDC replication feature](../../dlt-ecosystem/verified-sources/pg_replication), which is
+ useful for tracking changes and deletions in the data.
+
+### Sync Multiple Tables in Parallel
+To sync multiple tables in parallel, map each table copy job to a separate container using [Modal.starmap](https://modal.com/docs/reference/modal.Function#starmap):
+
+```py
+@app.function(timeout=3000, schedule=modal.Cron("29 11 * * *"))
+def main(dev: bool = False):
+    tables = [
+        ("task", "enqueued_at", dev),
+        ("worker", "launched_at", dev),
+        ...
+    ]
+    list(load_table_from_database.starmap(tables))
+```
\ No newline at end of file
diff --git a/docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-prefect.md b/docs/website/docs/walkthroughs/deploy_a_pipeline/deploy-with-prefect.md
similarity index 100%
rename from docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-prefect.md
rename to docs/website/docs/walkthroughs/deploy_a_pipeline/deploy-with-prefect.md
diff --git a/docs/website/docs/walkthroughs/deploy-a-pipeline/images/add-credential.png b/docs/website/docs/walkthroughs/deploy_a_pipeline/images/add-credential.png
similarity index 100%
rename from docs/website/docs/walkthroughs/deploy-a-pipeline/images/add-credential.png
rename to docs/website/docs/walkthroughs/deploy_a_pipeline/images/add-credential.png
diff --git a/docs/website/docs/walkthroughs/deploy-a-pipeline/images/dag-folder.png b/docs/website/docs/walkthroughs/deploy_a_pipeline/images/dag-folder.png
similarity index 100%
rename from docs/website/docs/walkthroughs/deploy-a-pipeline/images/dag-folder.png
rename to docs/website/docs/walkthroughs/deploy_a_pipeline/images/dag-folder.png
diff --git a/docs/website/docs/walkthroughs/deploy-a-pipeline/images/prefect-dashboard.png b/docs/website/docs/walkthroughs/deploy_a_pipeline/images/prefect-dashboard.png
similarity index 100%
rename from docs/website/docs/walkthroughs/deploy-a-pipeline/images/prefect-dashboard.png
rename to docs/website/docs/walkthroughs/deploy_a_pipeline/images/prefect-dashboard.png
diff --git a/docs/website/docs/walkthroughs/deploy-a-pipeline/images/prefect-flow-run.png b/docs/website/docs/walkthroughs/deploy_a_pipeline/images/prefect-flow-run.png
similarity index 100%
rename from docs/website/docs/walkthroughs/deploy-a-pipeline/images/prefect-flow-run.png
rename to docs/website/docs/walkthroughs/deploy_a_pipeline/images/prefect-flow-run.png
diff --git a/docs/website/docs/walkthroughs/deploy-a-pipeline/images/run-trigger.png b/docs/website/docs/walkthroughs/deploy_a_pipeline/images/run-trigger.png
similarity index 100%
rename from docs/website/docs/walkthroughs/deploy-a-pipeline/images/run-trigger.png
rename to docs/website/docs/walkthroughs/deploy_a_pipeline/images/run-trigger.png
diff --git a/docs/website/docs/walkthroughs/run-a-pipeline.md b/docs/website/docs/walkthroughs/run-a-pipeline.md
index 0be66b448b..3e946e7171 100644
--- a/docs/website/docs/walkthroughs/run-a-pipeline.md
+++ b/docs/website/docs/walkthroughs/run-a-pipeline.md
@@ -270,6 +270,6 @@ using the **JOB file path** provided.
 
 - [Beef up your script for production](../running-in-production/running.md), easily add alerting,
   retries, and logging, so you are well-informed when something goes wrong.
-- [Deploy this pipeline with GitHub Actions](deploy-a-pipeline/deploy-with-github-actions), so that
+- [Deploy this pipeline with GitHub Actions](deploy_a_pipeline/deploy-with-github-actions), so that
   your pipeline script is automatically executed on a schedule.
 
diff --git a/docs/website/sidebars.js b/docs/website/sidebars.js
index a87616990b..5cb3677273 100644
--- a/docs/website/sidebars.js
+++ b/docs/website/sidebars.js
@@ -271,17 +271,18 @@ const sidebars = {
             type: 'generated-index',
             title: 'Deploy a pipeline',
             description: 'Deploy dlt pipelines with different methods.',
-            slug: 'walkthroughs/deploy-a-pipeline',
+            slug: 'walkthroughs/deploy_a_pipeline',
           },
           items: [
-            'walkthroughs/deploy-a-pipeline/deploy-with-github-actions',
-            'walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer',
+            'walkthroughs/deploy_a_pipeline/deploy-with-github-actions',
+            'walkthroughs/deploy_a_pipeline/deploy-with-airflow-composer',
             'reference/explainers/airflow-gcp-cloud-composer',
-            'walkthroughs/deploy-a-pipeline/deploy-with-google-cloud-functions',
-            'walkthroughs/deploy-a-pipeline/deploy-gcp-cloud-function-as-webhook',
-            'walkthroughs/deploy-a-pipeline/deploy-with-kestra',
-            'walkthroughs/deploy-a-pipeline/deploy-with-dagster',
-            'walkthroughs/deploy-a-pipeline/deploy-with-prefect',
+            'walkthroughs/deploy_a_pipeline/deploy-with-google-cloud-functions',
+            'walkthroughs/deploy_a_pipeline/deploy-gcp-cloud-function-as-webhook',
+            'walkthroughs/deploy_a_pipeline/deploy-with-kestra',
+            'walkthroughs/deploy_a_pipeline/deploy-with-dagster',
+            'walkthroughs/deploy_a_pipeline/deploy-with-prefect',
+            'walkthroughs/deploy_a_pipeline/deploy-with-modal',
           ]
         },
         {
diff --git a/poetry.lock b/poetry.lock
index 8e3c8a2855..10e3672855 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
 [[package]]
 name = "about-time"
@@ -214,6 +214,20 @@ files = [
 [package.dependencies]
 frozenlist = ">=1.1.0"
 
+[[package]]
+name = "aiostream"
+version = "0.5.2"
+description = "Generator-based operators for asynchronous iteration"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "aiostream-0.5.2-py3-none-any.whl", hash = "sha256:054660370be9d37f6fe3ece3851009240416bd082e469fd90cc8673d3818cf71"},
+    {file = "aiostream-0.5.2.tar.gz", hash = "sha256:b71b519a2d66c38f0872403ab86417955b77352f08d9ad02ad46fc3926b389f4"},
+]
+
+[package.dependencies]
+typing-extensions = "*"
+
 [[package]]
 name = "alembic"
 version = "1.13.2"
@@ -2761,6 +2775,26 @@ files = [
 [package.extras]
 test = ["pytest (>=6)"]
 
+[[package]]
+name = "fastapi"
+version = "0.115.0"
+description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "fastapi-0.115.0-py3-none-any.whl", hash = "sha256:17ea427674467486e997206a5ab25760f6b09e069f099b96f5b55a32fb6f1631"},
+    {file = "fastapi-0.115.0.tar.gz", hash = "sha256:f93b4ca3529a8ebc6fc3fcf710e5efa8de3df9b41570958abf1d97d843138004"},
+]
+
+[package.dependencies]
+pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.1.0 || >2.1.0,<3.0.0"
+starlette = ">=0.37.2,<0.39.0"
+typing-extensions = ">=4.8.0"
+
+[package.extras]
+all = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.7)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
+standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "jinja2 (>=2.11.2)", "python-multipart (>=0.0.7)", "uvicorn[standard] (>=0.12.0)"]
+
 [[package]]
 name = "fastembed"
 version = "0.2.6"
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 ]
 
 [[package]]
@@ -4086,6 +4220,23 @@ grpcio = ">=1.57.0"
 protobuf = ">=4.21.6,<5.0dev"
 setuptools = "*"
 
+[[package]]
+name = "grpclib"
+version = "0.4.7"
+description = "Pure-Python gRPC implementation for asyncio"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "grpclib-0.4.7.tar.gz", hash = "sha256:2988ef57c02b22b7a2e8e961792c41ccf97efc2ace91ae7a5b0de03c363823c3"},
+]
+
+[package.dependencies]
+h2 = ">=3.1.0,<5"
+multidict = "*"
+
+[package.extras]
+protobuf = ["protobuf (>=3.20.0)"]
+
 [[package]]
 name = "gunicorn"
 version = "21.2.0"
@@ -4121,7 +4272,7 @@ files = [
 name = "h2"
 version = "4.1.0"
 description = "HTTP/2 State-Machine based protocol implementation"
-optional = true
+optional = false
 python-versions = ">=3.6.1"
 files = [
     {file = "h2-4.1.0-py3-none-any.whl", hash = "sha256:03a46bcf682256c95b5fd9e9a99c1323584c3eec6440d379b9903d709476bc6d"},
@@ -4153,7 +4304,7 @@ test = ["eth-utils (>=1.0.1,<3)", "hypothesis (>=3.44.24,<=6.31.6)", "pytest (>=
 name = "hpack"
 version = "4.0.0"
 description = "Pure-Python HPACK header compression"
-optional = true
+optional = false
 python-versions = ">=3.6.1"
 files = [
     {file = "hpack-4.0.0-py3-none-any.whl", hash = "sha256:84a076fad3dc9a9f8063ccb8041ef100867b1878b25ef0ee63847a5d53818a6c"},
@@ -4283,7 +4434,7 @@ tests = ["freezegun", "pytest", "pytest-cov"]
 name = "hyperframe"
 version = "6.0.1"
 description = "HTTP/2 framing layer for Python"
-optional = true
+optional = false
 python-versions = ">=3.6.1"
 files = [
     {file = "hyperframe-6.0.1-py3-none-any.whl", hash = "sha256:0ec6bafd80d8ad2195c4f03aacba3a8265e57bc4cff261e802bf39970ed02a15"},
@@ -5349,6 +5500,33 @@ files = [
 [package.extras]
 test = ["mypy (>=1.0)", "pytest (>=7.0.0)"]
 
+[[package]]
+name = "modal"
+version = "0.64.178"
+description = "Python client library for Modal"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "modal-0.64.178-py3-none-any.whl", hash = "sha256:89c40c35aed472d0f831a17654a07176d4c9a7c5d3e4fcd9ef40d8dead762879"},
+]
+
+[package.dependencies]
+aiohttp = "*"
+aiostream = ">=0.5.2,<0.6.0"
+certifi = "*"
+click = ">=8.1.0"
+fastapi = "*"
+grpclib = "0.4.7"
+protobuf = ">=3.19,<4.24.0 || >4.24.0,<5.0"
+rich = ">=12.0.0"
+synchronicity = ">=0.8.2,<0.9.0"
+toml = "*"
+typer = ">=0.9"
+types-certifi = "*"
+types-toml = "*"
+typing-extensions = ">=4.6,<5.0"
+watchfiles = "*"
+
 [[package]]
 name = "more-itertools"
 version = "10.1.0"
@@ -8355,6 +8533,35 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments
 testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
 testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
 
+[[package]]
+name = "shellingham"
+version = "1.5.4"
+description = "Tool to Detect Surrounding Shell"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "shellingham-1.5.4-py2.py3-none-any.whl", hash = "sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686"},
+    {file = "shellingham-1.5.4.tar.gz", hash = "sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de"},
+]
+
+[[package]]
+name = "sigtools"
+version = "4.0.1"
+description = "Utilities for working with inspect.Signature objects."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "sigtools-4.0.1-py2.py3-none-any.whl", hash = "sha256:d216b4cf920bbab0fce636ddc429ed8463a5b533d9e1492acb45a2a1bc36ac6c"},
+    {file = "sigtools-4.0.1.tar.gz", hash = "sha256:4b8e135a9cd4d2ea00da670c093372d74e672ba3abb87f4c98d8e73dea54445c"},
+]
+
+[package.dependencies]
+attrs = "*"
+
+[package.extras]
+test = ["coverage", "mock", "repeated-test (>=2.2.1)", "sphinx"]
+tests = ["coverage", "mock", "repeated-test (>=2.2.1)", "sphinx"]
+
 [[package]]
 name = "simplejson"
 version = "3.19.1"
@@ -8741,6 +8948,24 @@ dev = ["build", "flake8"]
 doc = ["sphinx"]
 test = ["pytest", "pytest-cov"]
 
+[[package]]
+name = "starlette"
+version = "0.38.6"
+description = "The little ASGI library that shines."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "starlette-0.38.6-py3-none-any.whl", hash = "sha256:4517a1409e2e73ee4951214ba012052b9e16f60e90d73cfb06192c19203bbb05"},
+    {file = "starlette-0.38.6.tar.gz", hash = "sha256:863a1588f5574e70a821dadefb41e4881ea451a47a3cd1b4df359d4ffefe5ead"},
+]
+
+[package.dependencies]
+anyio = ">=3.4.0,<5"
+typing-extensions = {version = ">=3.10.0", markers = "python_version < \"3.10\""}
+
+[package.extras]
+full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7)", "pyyaml"]
+
 [[package]]
 name = "stevedore"
 version = "5.1.0"
@@ -8769,6 +8994,20 @@ files = [
 [package.dependencies]
 mpmath = ">=0.19"
 
+[[package]]
+name = "synchronicity"
+version = "0.8.2"
+description = "Export blocking and async library versions from a single async implementation"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "synchronicity-0.8.2-py3-none-any.whl", hash = "sha256:87c2adc3f388b80629e55d202bd8404b17cf28b857a58c0094bf3ab093d430c6"},
+]
+
+[package.dependencies]
+sigtools = "4.0.1"
+typing-extensions = ">=4.6"
+
 [[package]]
 name = "tabulate"
 version = "0.9.0"
@@ -9094,6 +9333,23 @@ files = [
 [package.dependencies]
 typing-extensions = ">=3.0.0"
 
+[[package]]
+name = "typer"
+version = "0.12.5"
+description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "typer-0.12.5-py3-none-any.whl", hash = "sha256:62fe4e471711b147e3365034133904df3e235698399bc4de2b36c8579298d52b"},
+    {file = "typer-0.12.5.tar.gz", hash = "sha256:f592f089bedcc8ec1b974125d64851029c3b1af145f04aca64d69410f0c9b722"},
+]
+
+[package.dependencies]
+click = ">=8.0.0"
+rich = ">=10.11.0"
+shellingham = ">=1.3.0"
+typing-extensions = ">=3.7.4.3"
+
 [[package]]
 name = "types-awscrt"
 version = "0.19.1"
@@ -9116,6 +9372,17 @@ files = [
     {file = "types_cachetools-5.3.0.6-py3-none-any.whl", hash = "sha256:f7f8a25bfe306f2e6bc2ad0a2f949d9e72f2d91036d509c36d3810bf728bc6e1"},
 ]
 
+[[package]]
+name = "types-certifi"
+version = "2021.10.8.3"
+description = "Typing stubs for certifi"
+optional = false
+python-versions = "*"
+files = [
+    {file = "types-certifi-2021.10.8.3.tar.gz", hash = "sha256:72cf7798d165bc0b76e1c10dd1ea3097c7063c42c21d664523b928e88b554a4f"},
+    {file = "types_certifi-2021.10.8.3-py3-none-any.whl", hash = "sha256:b2d1e325e69f71f7c78e5943d410e650b4707bb0ef32e4ddf3da37f54176e88a"},
+]
+
 [[package]]
 name = "types-click"
 version = "7.1.8"
@@ -9273,6 +9540,17 @@ files = [
     {file = "types_SQLAlchemy-1.4.53.38-py3-none-any.whl", hash = "sha256:7e60e74f823931cc9a9e8adb0a4c05e5533e6708b8a266807893a739faf4eaaa"},
 ]
 
+[[package]]
+name = "types-toml"
+version = "0.10.8.20240310"
+description = "Typing stubs for toml"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "types-toml-0.10.8.20240310.tar.gz", hash = "sha256:3d41501302972436a6b8b239c850b26689657e25281b48ff0ec06345b8830331"},
+    {file = "types_toml-0.10.8.20240310-py3-none-any.whl", hash = "sha256:627b47775d25fa29977d9c70dc0cbab3f314f32c8d8d0c012f2ef5de7aaec05d"},
+]
+
 [[package]]
 name = "types-tqdm"
 version = "4.66.0.2"
@@ -9454,6 +9732,101 @@ files = [
 [package.extras]
 watchmedo = ["PyYAML (>=3.10)"]
 
+[[package]]
+name = "watchfiles"
+version = "0.24.0"
+description = "Simple, modern and high performance file watching and code reload in python."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "watchfiles-0.24.0-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:083dc77dbdeef09fa44bb0f4d1df571d2e12d8a8f985dccde71ac3ac9ac067a0"},
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+    {file = "watchfiles-0.24.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:01550ccf1d0aed6ea375ef259706af76ad009ef5b0203a3a4cce0f6024f9b68a"},
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+    {file = "watchfiles-0.24.0-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:85d5f0c7771dcc7a26c7a27145059b6bb0ce06e4e751ed76cdf123d7039b60b5"},
+    {file = "watchfiles-0.24.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:951088d12d339690a92cef2ec5d3cfd957692834c72ffd570ea76a6790222777"},
+    {file = "watchfiles-0.24.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:49fb58bcaa343fedc6a9e91f90195b20ccb3135447dc9e4e2570c3a39565853e"},
+    {file = "watchfiles-0.24.0.tar.gz", hash = "sha256:afb72325b74fa7a428c009c1b8be4b4d7c2afedafb2982827ef2156646df2fe1"},
+]
+
+[package.dependencies]
+anyio = ">=3.0.0"
+
 [[package]]
 name = "wcwidth"
 version = "0.2.6"
@@ -9655,16 +10028,18 @@ email = ["email-validator"]
 
 [[package]]
 name = "yapf"
-version = "0.33.0"
-description = "A formatter for Python code."
+version = "0.40.2"
+description = "A formatter for Python code"
 optional = false
-python-versions = "*"
+python-versions = ">=3.7"
 files = [
-    {file = "yapf-0.33.0-py2.py3-none-any.whl", hash = "sha256:4c2b59bd5ffe46f3a7da48df87596877189148226ce267c16e8b44240e51578d"},
-    {file = "yapf-0.33.0.tar.gz", hash = "sha256:da62bdfea3df3673553351e6246abed26d9fe6780e548a5af9e70f6d2b4f5b9a"},
+    {file = "yapf-0.40.2-py3-none-any.whl", hash = "sha256:adc8b5dd02c0143108878c499284205adb258aad6db6634e5b869e7ee2bd548b"},
+    {file = "yapf-0.40.2.tar.gz", hash = "sha256:4dab8a5ed7134e26d57c1647c7483afb3f136878b579062b786c9ba16b94637b"},
 ]
 
 [package.dependencies]
+importlib-metadata = ">=6.6.0"
+platformdirs = ">=3.5.1"
 tomli = ">=2.0.1"
 
 [[package]]
@@ -9861,4 +10236,4 @@ weaviate = ["weaviate-client"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<3.13"
-content-hash = "11385c8ff3ce09de74da03658d4e81cdc3bd991556d715d69dbc1e17b54a1d91"
+content-hash = "5e437e5bf4ce253d04bc47eacc4edd3b966b00c66ee23c1933332cee8400e2d6"
diff --git a/pyproject.toml b/pyproject.toml
index 2bd28cdb36..db9bdc7446 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -243,6 +243,7 @@ psycopg2-binary = ">=2.9"
 lancedb = { version = ">=0.8.2", markers = "python_version >= '3.9'", allow-prereleases = true }
 openai = ">=1.45"
 connectorx = { version = ">=0.3.2" }
+modal = ">=0.64.170"
 
 [tool.black] # https://black.readthedocs.io/en/stable/usage_and_configuration/the_basics.html#configuration-via-a-file
 line-length = 100