Add a new tutorial which shows how to debug a failing pipeline (#16225)

Co-authored-by: Jeff Hale <discdiver@users.noreply.github.com>

Author: daniel-prefect

docs/mint.json

@@ -73,7 +73,8 @@
         {
           "group": "For platform engineers",
           "pages": [
-            "v3/tutorials/platform"
+            "v3/tutorials/platform",
+            "v3/tutorials/debug"
           ]
         }
       ],

docs/v3/tutorials/debug.mdx

@@ -0,0 +1,116 @@
+---
+title: Debug a data pipeline
+description: Learn how to troubleshoot flow runs that fail.
+---
+
+In the [Set up a platform for data pipelines](/v3/tutorials/platform) tutorial, you used Prefect Cloud to set up a platform for data pipelines.
+In this tutorial, you'll learn what to do when those data pipelines fail.
+
+<Info>
+This tutorial starts where the [previous tutorial](/v3/tutorials/platform) leaves off, so complete that one first.
+</Info>
+
+## Find failures
+
+You can use the Prefect Cloud dashboard to find failures.
+
+1. Sign in to Prefect Cloud
+1. Use the workspace switcher to open the `staging` workspace that you created in the last tutorial.
+1. Go to **Home**, and look for red bars in the **Flow Runs** section, these indicate failed flow runs.
+1. Hover over a red bar to see more details about the flow run: name, deployment, duration, timestamp, and tags.
+
+<Note>
+You can filter by a specific tag (e.g. `team-a`) if you're only interested in a specific set of flows.
+</Note>
+
+## Debug a failure
+
+A single flow might experience failures on several runs.
+When this happens, it can be helpful to inspect the first failure in the series.
+
+1. In the **Flow Runs** section on the **Home** page, expand the `data-pipeline` flow.
+1. You will see a list of failing `data-pipeline` flow runs, in reverse chronological order.
+1. Use the pagination controls to navigate to the last failure in the list, this is the first failure that occurred.
+1. Click the name of the flow run to go to its detail page.
+1. From the flow run detail page, scroll down to the **Logs** section in the right panel.
+1. Look for an error message similar to the following:
++
+```
+File "/opt/prefect/demos/simulate_failures.py", line 12, in process_data
+    raise Exception(f"Run failed")
+```
+
+It looks like there's an error in the `simulate_failures.py` file.
+Now that you've found the failure, the next step is to fix the underlying code.
+
+## Update the code
+
+Open the `simulate_failures.py` file and look at line 12.
+
+```python simulate_failures.py {12}
+from prefect import flow, task
+import argparse
+import asyncio
+from prefect.client.orchestration import get_client
+
+
+@task
+def process_data(run: int, fail_at_run: int | None = None) -> bool:
+    """Simulate data processing with failures"""
+    
+    # Simulate persistent failures
+    if fail_at_run and run > fail_at_run:
+        raise Exception(f"Run failed")
+    
+    return True
+
+# ...
+```
+
+The `if` statement is the problem.
+If you specify the `--fail_at_run` flag, once the flow runs more than `fail_at_run` times, the flow fails with an exception.
+Remove the `if` statement to fix this failure.
+We added this statement to give you something to fix. :) 
+
+```python simulate_failures.py
+from prefect import flow, task
+import argparse
+import asyncio
+from prefect.client.orchestration import get_client
+
+@task
+def process_data(run: int, fail_at_run: int | None = None) -> bool:
+    """Simulate data processing with failures"""
+    
+    return True
+
+# ...
+```
+
+Now, all flow runs succeed in spite of the `--fail-at-run` flag.
+Deploy the fix to the staging workspace to confirm this new behavior.
+
+```bash
+prefect cloud workspace --set "<account>/staging"
+python simulate_failures.py --fail-at-run 3
+```
+
+After the script finishes, open the **Home** page in Prefect Cloud to verify that the flow run is no longer failing.
+
+You can now switch workspaces to update the code used in the production workspace as well.
+
+```bash
+prefect cloud workspace --set "<account>/production"
+python simulate_failures.py
+```
+
+
+## Next steps
+
+In this tutorial, you successfully used Prefect Cloud to fix a failing data pipeline.
+
+To take this to the next level, learn how to [set up an alert](/v3/automate/events/automations-triggers) so that you get notified about failures automatically.
+
+<Tip>
+Need help? [Book a meeting](https://calendly.com/prefect-experts/prefect-product-advocates?utm_campaign=prefect_docs_cloud&utm_content=prefect_docs&utm_medium=docs&utm_source=docs) with a Prefect Product Advocate to get your questions answered.
+</Tip>

docs/v3/tutorials/platform.mdx

@@ -188,6 +188,8 @@ If this doesn't perfectly match your use case, here are some variations you can
 - You can [write flows from scratch](/v3/develop/write-flows).
 - You can [automate deployments with GitHub Actions](/v3/deploy/infrastructure-concepts/deploy-ci-cd).
 
+Next, learn how to [debug a flow run](/v3/tutorials/debug) when things go wrong.
+
 <Tip>
 Need help? [Book a meeting](https://calendly.com/prefect-experts/prefect-product-advocates?utm_campaign=prefect_docs_cloud&utm_content=prefect_docs&utm_medium=docs&utm_source=docs) with a Prefect Product Advocate to get your questions answered.
 </Tip>