[docs] include link to airlift video in tutorial (#26252)

## Summary & Motivation

Let me know if you'd prefer it here, or in
https://docs.dagster.io/integrations/airlift#airlift

<img width="1176" alt="image"
src="https://github.com/user-attachments/assets/1d5f61aa-14d3-4ab9-8a93-254a33632bcd">

## How I Tested These Changes

## Changelog

> Insert changelog entry or delete this section.

docs/content/integrations/airlift.mdx

@@ -7,6 +7,21 @@ Airlift is a toolkit for integrating Dagster and Airflow.
 
 ---
 
+## Introduction
+
+Watch a quick introduction of Airlift from [Data Platform Week](https://dagster.io/events/data-platform-week).
+
+<iframe
+  width="560"
+  height="315"
+  src="https://www.youtube.com/embed/jmdars2L7nc?si=AtfCW3GmI73zskp6&amp;start=68"
+  title="YouTube video player"
+  frameborder="0"
+  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+  referrerpolicy="strict-origin-when-cross-origin"
+  allowfullscreen
+></iframe>
+
 ## Compatibility
 
 ### REST API Availability

docs/content/integrations/airlift/tutorial.mdx

@@ -2,21 +2,6 @@
 
 This example demonstrates how to migrate an Airflow DAG to Dagster using the `dagster-airlift` package. It contains code examples of how to peer, observe, and migrate assets from an Airflow DAG to Dagster. The below guide will walk through each of these steps in detail.
 
-## Example Structure
-
-```plaintext
-tutorial_example
-├── shared: Contains shared Python & SQL code used Airflow and proxied Dagster code
-│
-├── dagster_defs: Contains Dagster definitions
-│   ├── stages: Contains reference implementations of each stage of the migration process
-│   ├── definitions.py: Empty starter file for following along with the tutorial
-│
-├── airflow_dags: Contains the Airflow DAG and associated files
-│   ├── proxied_state: Contains migration state files for each DAG, see migration step below
-│   ├── dags.py: The Airflow DAG definition
-```
-
 ## Guide
 
 This tutorial will walk through the process of peering, observing, and migrating assets from an Airflow DAG to Dagster.