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
new file mode 100644
index 0000000000..6fbf0f0d16
--- /dev/null
+++ b/docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-prefect.md
@@ -0,0 +1,69 @@
+---
+title: Deploy with Prefect
+description: How to deploy a pipeline with Prefect
+keywords: [how to, deploy a pipeline, Prefect]
+---
+
+# Deploy with Prefect
+
+## Introduction to Prefect
+
+Prefect is a workflow management system that automates and orchestrates data pipelines. As an open-source platform, it offers a framework for defining, scheduling, and executing tasks with dependencies. It enables users to scale and maintain their data workflows efficiently.
+
+### Prefect features
+
+- **Flows**:  These contain workflow logic, and are defined as Python functions.
+- **Tasks**:  A task represents a discrete unit of work. Tasks allow encapsulation of workflow logic that can be reused for flows and subflows.
+- **Deployments and Scheduling**:  Deployments transform workflows from manually called functions into API-managed entities that you can trigger remotely. Prefect allows you to use schedules to automatically create new flow runs for deployments.
+- **Automation:** Prefect Cloud enables you to configure [actions](https://docs.prefect.io/latest/concepts/automations/#actions) that Prefect executes automatically based on [trigger](https://docs.prefect.io/latest/concepts/automations/#triggers) conditions.
+- **Caching:** This feature enables a task to reflect a completed state without actually executing its defining code.
+- **Oberservality**: This feature allows users to monitor workflows and tasks. It provides insights into data pipeline performance and behavior through logging, metrics, and notifications.
+
+## Building Data Pipelines with `dlt`
+
+`dlt` is an open-source Python library that enables the declarative loading of data sources into well-structured tables or datasets by automatically inferring and evolving schemas. It simplifies the construction of data pipelines by offering functionality to support the complete extract and load process.
+
+### How does **`dlt`** integrate with Prefect for pipeline orchestration?
+
+Here's a concise guide to orchestrating a `dlt` pipeline with Prefect using "Moving Slack data into BigQuery" as an example. You can find a comprehensive, step-by-step guide in the article [“Building resilient data pipelines in minutes with dlt + Prefect”,](https://www.prefect.io/blog/building-resilient-data-pipelines-in-minutes-with-dlt-prefect) and the corresponding GitHub repository [here.](https://github.com/dylanbhughes/dlt_slack_pipeline/blob/main/slack_pipeline_with_prefect.py)
+
+### Here’s a summary of the steps followed:
+
+1. Create a `dlt` pipeline. For detailed instructions on creating a pipeline, please refer to the [documentation](https://dlthub.com/docs/walkthroughs/create-a-pipeline).
+
+1. Add `@task` decorator to the individual functions.
+    1. Here we use `@task` decorator for `get_users` function: 
+        
+        ```py
+        @task
+        def get_users() -> None:
+            """Execute a pipeline that will load Slack users list."""
+        ```
+        
+    1. Use `@flow` function on the `slack_pipeline` function as:
+        
+        ```py
+        @flow
+        def slack_pipeline(
+            channels=None, 
+            start_date=pendulum.now().subtract(days=1).date()
+        ) -> None:
+            get_users()
+        
+        ```
+        
+2. Lastly, append `.serve` to the `if __name__ == '__main__'` block to automatically create and schedule a Prefect deployment for daily execution as:
+    
+    ```py
+    if __name__ == "__main__":
+        slack_pipeline.serve("slack_pipeline", cron="0 0 * * *")
+    ```
+    
+3. You can view deployment details and scheduled runs, including successes and failures, using [PrefectUI](https://app.prefect.cloud/auth/login). This will help you know when a pipeline ran or more importantly, when it did not.
+
+
+You can further extend the pipeline further by: 
+
+- Setting up [remote infrastructure with workers.](https://docs.prefect.io/latest/tutorial/workers/?deviceId=bb3e22c1-c2c7-4981-bd5e-c81715503e08)
+- [Adding automations](https://docs.prefect.io/latest/concepts/automations/?deviceId=bb3e22c1-c2c7-4981-bd5e-c81715503e08), to notify the status of pipeline run.
+- [Setting up retries](https://docs.prefect.io/latest/concepts/tasks/?deviceId=bb3e22c1-c2c7-4981-bd5e-c81715503e08#custom-retry-behavior).
diff --git a/docs/website/sidebars.js b/docs/website/sidebars.js
index 418ac2efd6..2f2c444286 100644
--- a/docs/website/sidebars.js
+++ b/docs/website/sidebars.js
@@ -235,6 +235,7 @@ const sidebars = {
             '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',
           ]
         },
         {