I need help setting up an automated pipeline using Google Cloud services. My goal is to use Cloud Scheduler to automatically trigger a notebook running on a VM instance. The notebook should connect to BigQuery to pull data, transform it, and then save the results to Cloud Storage.
I already tried using Cloud Functions but ran into timeout issues with my data processing tasks. That’s why I’m looking into running notebooks on compute instances instead. Has anyone successfully set up this kind of automated workflow? What’s the best way to configure the scheduler to communicate with a notebook on a VM?
Any guidance or examples would be really helpful. Thanks!
Been down this exact road before and you’re overcomplicating things with the GCP native approach.
The scheduler to VM to notebook setup is a pain to maintain. You’ll need to handle VM startup delays, notebook execution monitoring, error handling, and state management across multiple services.
Automating these multi-step workflows is way cleaner with a proper automation platform. Instead of juggling Cloud Scheduler, Compute Engine, and custom scripts, build the entire pipeline in one place.
I create workflows that handle BigQuery connections, data transformations, and Cloud Storage uploads all in sequence. No timeout issues like Cloud Functions, and way more reliable than managing notebook instances.
You get proper error handling, retry logic, and monitoring built in. Plus you can modify workflows without touching VM configurations or scheduler settings.
For your use case, just set up the trigger schedule, connect to BigQuery with your transformations, and pipe everything to Cloud Storage. Much simpler than the VM route.
Check out Latenode for this automation: https://latenode.com
Finally got this working after weeks of fighting with it. Use Cloud Scheduler to ping a simple HTTP endpoint on your VM - that’s what triggers the notebook. Here’s what you need: install jupyter-client on the VM and build a basic Flask app that catches scheduler requests and runs notebooks via nbconvert or papermill. Set up a startup script so both the notebook server and trigger endpoint launch automatically. Biggest gotcha? Authentication. You’ll need service account keys for BigQuery and proper firewall rules. Also - and I learned this the hard way - build in logging and status checks since the scheduler needs to know if jobs actually worked. This beats Cloud Functions hands down. Way more control over resources and processing time, plus you can adjust VM specs based on how much data you’re crunching.