Has anyone integrated OpenPipe with Snowflake or BigQuery successfully?

You've cut your connection config example off at "rol" which might be part of the timeout problem. I've seen the service account role need explicit session and warehouse usage grants beyond simple connection. For batch timeouts, check the network policy in Snowflake and the idle timeout setting on your warehouse. It's often not an OpenPipe issue but a warehouse config one. Did you set up a specific user resource monitor?

Yeah, that truncated role config is a classic gotcha. I've seen that exact thing cause intermittent timeouts because the connection can't properly acquire the warehouse.

For BigQuery service accounts, make sure you're granting the "BigQuery Data Editor" role at a minimum, but also double-check the "BigQuery Job User" role. Without that, the service account can see data but can't actually run the transformation jobs OpenPipe kicks off.

A quick test: try running a tiny, one-row transform in OpenPipe as a smoke test for the permissions. If that works but your batch job dies, user1000 is probably right about the warehouse idle timeout.

Good point about the one-row smoke test for permissions. I had a similar issue where the test passed but a larger job failed, and it turned out to be a quota limit on concurrent queries for the service account in BigQuery. Maybe worth checking that too.

That's an excellent catch on the quota limit. It's one of those silent failure points that's easy to miss because the permissions structure looks correct. I've found the BigQuery admin API quotas and the concurrent interactive query slots to be the usual culprits for that specific scenario.

A related nuance is that if you're using scheduled batch transformations in OpenPipe, they might inherit the default project's quota limits instead of the targeted dataset's project, depending on how your service account and resource hierarchy are configured. Always verify quotas in the actual project where the job executes.

Hey, great question. The truncated role config you mentioned is a classic culprit for those batch timeouts. It often means the service user can't properly assume the warehouse role.

For Snowflake, beyond the role, double-check the warehouse's `auto_suspend` and `auto_resume` settings. If your batch job has long pauses between queries, the warehouse might be suspending mid-session. Setting `auto_suspend` to a higher value (or 0 for long jobs) in the warehouse definition can fix this.

For BigQuery, the advice here on quotas is spot on. The smoke test is key.

Good call on the smoke test. It's a great first step, but you're right that it can pass while production loads still fail.

Those quota limits in BigQuery are particularly tricky because they often don't throw clear access-denied errors, just silent queuing or throttling. I'd recommend checking not just concurrent query quotas, but also the *rate limits* for API methods like `jobs.insert`. A burst of transformation jobs can hit those caps fast.

You might also see this if your OpenPipe flow triggers parallel branches that all try to query the same dataset simultaneously.

That truncated `"rol` field is almost certainly your root problem for the connection instability. The service principal likely doesn't have a role assigned, so it's defaulting to `PUBLIC` and can't use the `TRANSFORM_WH` warehouse. The connection might succeed for a session test, but any actual workload will hang until it times out.

For your specific hybrid setup, I'd forget the JSON config for a moment and validate the principal's access directly in Snowflake. Run this as an admin for your service user:

```sql
SHOW GRANTS TO USER OPENPIPE_SVC;
```

You need to see a `USAGE` grant on the warehouse and the `PUBLIC` schema, and likely `OWNERSHIP` on a dedicated schema for transformations. If you're missing the warehouse grant, that's your smoking gun.

On the BigQuery side for your new analytics pod, preempt the quota issues others mentioned by setting up a dedicated quota profile for the OpenPipe service account in GCP, bumping the concurrent interactive query slots. Don't rely on the default project limits.

Your direct validation approach is correct, but I'd add that `OWNERSHIP` on a dedicated schema can be overkill and create future security drift. Grant `CREATE TABLE` and `USAGE` on the schema, plus `ALL` on the future tables, via a custom role. This avoids the service account having rights to drop the schema itself.

For BigQuery, setting up a dedicated quota profile is good advice, but remember that those adjustments can take 15-20 minutes to propagate across Google's systems. I've seen teams trigger jobs immediately after the change and hit the old limits.

> `OWNERSHIP` on a dedicated schema can be overkill

Agree completely. I've had to clean up that exact mess after a service account with OWNERSHIP was deprovisioned and the automated schema cleanup in our pipelines failed.

A tighter setup is a custom role for the warehouse, like `OPENPIPE_TRANSFORM_ROLE`. Grant it:
- `USAGE` on the warehouse
- `CREATE TABLE` and `USAGE` on the target schema
- `ALL PRIVILEGES` on FUTURE TABLES IN SCHEMA

This keeps the service account from modifying or dropping the schema object itself, which is a separation you want.

The quota propagation delay is real. We script quota increases to run well before the scheduled pipeline, and then verify with a test query to the `INFORMATION_SCHEMA.JOBS` view in BigQuery to confirm the new limit is active.

Completely agree on avoiding `OWNERSHIP`. It's so easy to accidentally script a `DROP SCHEMA CASCADE` later and not realize the service account can run it.

That propagation delay for BigQuery quotas is a real trap. We now schedule quota changes as a separate "pre-flight" task in our pipeline orchestration, at least 30 minutes before the main job. Even then, we've had it take closer to 45 minutes during Google's internal updates. A quick check against `INFORMATION_SCHEMA.JOBS_BY_PROJECT` for a recent, successful large job from the service account is our final confirmation before kicking things off.

Your point about the quota propagation delay is critical, and the 45-minute mark isn't even the worst I've seen. In one multi-region GCP setup, we observed a full 90-minute lag before the new limits were respected across all routing paths. The `INFORMATION_SCHEMA.JOBS_BY_PROJECT` check is the right diagnostic.

An additional layer we've added is monitoring the quota metrics themselves in Cloud Monitoring for the specific service account. You can set up an alert on the `serviceruntime.googleapis.com/quota/allocation/usage` metric with a filter for your quota name. When that graph plateaus at the new limit after a test job, it's a more definitive signal than just a successful job, which could have been routed through a different, already-updated backend cell. It adds maybe two more minutes to the pre-flight, but it's saved us from launching a job that would immediately throttle itself.

Monitoring the actual quota metric is a clever diagnostic layer I hadn't considered, though it feels like we're just building ever-more elaborate scaffolding to paper over a fundamentally frustrating system behavior. A 90-minute propagation delay for a quota increase in a paid service is frankly absurd.

I'd be curious if you've ever seen this latency manifest differently based on the *type* of quota. We've had times where an increase to the BigQuery API request quota applied quickly, but the concurrent interactive query slots took hours, all within the same project. Makes you wonder if the consistency of their internal control plane is as reliable as they claim.

You're absolutely right about the latency differing by quota type. We've logged the same pattern. Interactive slot increases took nearly two hours to stabilize, while the concurrent API requests limit updated in under ten minutes for the same project.

This points to the control planes being separate systems with their own propagation cycles. The API quota might be managed at a global front-end layer, while query slots are governed by the resource scheduler for a specific region or reservation. That inconsistency is what forces these complex pre-flight checks.

I've also seen the query slot increase *appear* to take effect quickly for a single job, but then fail under parallel load because the update hadn't replicated to all scheduler instances. Monitoring the quota metric was the only way to see it was still partially throttled.

Oh, that truncated `"rol` field in your config is a classic, it bit us too. Your service account is probably defaulting to `PUBLIC` role and can't actually use the `TRANSFORM_WH` warehouse.

Before you dive into the JSON again, run a quick check in Snowflake for your user:

```sql
SHOW GRANTS TO USER OPENPIPE_SVC;
```

Look for `USAGE` on the warehouse specifically. If it's missing, that's why your batch jobs timeout - the session connects but has nowhere to run the query.

For BigQuery, parallel loads can also hit quota limits silently. Are you checking the concurrent slots *and* the API rate limits for `jobs.insert`? A burst from OpenPipe can trip that.