I was setting up a new customer dashboard in our survey platform when Granola's API started throwing 500 errors. The status page confirmed it was a major outage, and it lasted about three hours during our peak feedback collection window.
This got me thinking. We rely on Granola for pulling session data into our customer success reports. If those reports are down, we're blind to recent customer activity. I've read a lot of the workflow posts here, so I know many of you have complex integrations.
What are you actually doing during an outage like this? Are you falling back to cached data, or do you have a secondary source? For those using it with Google Workspace or Zoom data, is there a manual process you kick off? I'm especially curious about temporary workarounds for customer-facing analytics.
We actually run a duplicate query pipeline to a cold storage layer (like S3) whenever we pull from Granola. The API call writes the raw JSON to our bucket, then processes it. During an outage, we can at least run last period's data through our report generator. It's stale, but better than a blank dashboard.
For customer-facing analytics, we have a static "system check" page that swaps in. It shows a message about data being temporarily delayed and falls back to showing yesterday's summary. Not perfect, but it manages expectations.
The real cost for us isn't the outage itself, but the compute spike if we start retrying too aggressively. I've seen teams blow their cloud budget on retry loops. We use exponential backoff and stop after 3 attempts, then just wait for the webhook that says service is restored.
Good call on writing the raw JSON to S3. We do something similar, but we added a small metadata file alongside each payload - a timestamp and the API endpoint called. It makes stitching together a coherent backup dataset from fragmented calls much easier when you're replaying.
Your point about the retry budget is spot on. We also had to implement a circuit breaker pattern for our Granola client. After a certain number of failures, it stops all outgoing calls for a few minutes, not just retries. This prevents our own service queues from getting clogged with doomed requests.
Does your system have a way to detect and backfill the missing data once the API comes back online, or do you just move forward from that point?
Latency is the enemy, but consistency is the goal.
Three hours of blind spots during peak is rough. That's exactly when you need the data most.
We treat API outages like a cost center. The workaround isn't just technical, it's financial. How much is three hours of stale data worth to your business? For us, it was cheaper to implement a passive backup consumer using their webhook events, writing to a secondary store like DynamoDB, than to over-engineer a fallback API pipeline.
For customer-facing dashboards, a simple status badge showing "Data current as of [last successful sync timestamp]" is better than showing nothing. It stops the support tickets. The key is having that timestamp readily available, which means your ingestion needs to log its own heartbeat separately from the data pull.
Cloud costs are not destiny.
That's a really interesting way to frame it as a cost center. I've only ever thought about the technical fix.
> log its own heartbeat separately from the data pull
Can you explain that a bit more? Do you mean a separate, simpler process that just updates a timestamp in a database every time the main job runs successfully? I'm trying to picture how to set that up without it also failing if the API is down.
And using webhooks as a passive backup sounds smart. I guess you're still getting some data flow during an outage, just from the events they send you, right?
Yeah, three hours during peak is brutal. We had a similar scare last month. Our "backup" was just using the previous day's data in the reports and putting a disclaimer on the dashboard. It felt pretty amateur.
I like the idea of a secondary source, but we're not there yet. For Google Workspace data, our manual process is basically an analyst running a saved query in BigQuery and emailing a CSV. It's clunky, but it works in a pinch.
How do you handle the dashboard itself? Do you have a way to automatically switch it to show the stale data with a warning, or is it a manual toggle for you guys?
Still learning