So you’ve hooked your shiny PowerBI dashboard up to the OneTrust API. Neat.
I pulled some metrics last week. The API is… fine. Latency spikes if you query more than a few thousand records at once. The `createdAfter` filter is your only real friend for incremental pulls. Here’s the basic curl I use from a scheduled pod before it gives up.
```bash
curl -s -H "Authorization: Bearer $TOKEN"
"https://$YOUR_DOMAIN.onetrust.com/api/asset/v1/assets?page=0&size=500&createdAfter=2024-01-01T00:00:00Z"
```
Hope you’ve got robust error handling. The token expires, the paging breaks, and sometimes you just get an empty 200. Enjoy maintaining that pipeline when the next “improved” API version drops and breaks everything.
If it ain't broke, don't 'upgrade' it.
That's a really practical warning. The latency you mentioned on larger queries is something I've run into as well, especially when trying to build historical baselines. I found the same thing with the `createdAfter` filter being essential, but even then, I had to batch by smaller time windows, not just page size, to keep the response times manageable.
You mentioned the token expiring and empty 200s. For the token, are you caching and refreshing it proactively, or are you fetching a new one for each scheduled pull? I've been debating which approach causes less headache.
And the "improved" API version point is painfully true. I'm already documenting every field mapping because I assume the next minor version will change a property name or nest something differently. Do you think it's worth wrapping the API calls in an abstraction layer from the start, or is that just premature complexity?
The token refresh question is a good one. I've been burned by fetching new tokens every time because our cron job would occasionally hit a rate limit. Caching with a proactive refresh feels cleaner, but then you've got to manage that cache's state somewhere, right? Adds another moving part.
>wrapping the API calls in an abstraction layer
I'd lean toward yes, but maybe keep it super thin at first? Like just a simple client that handles auth and the base URL. That way if a field name changes, you only have one place to update the mapping. I guess the complexity comes if you try to predict future changes too much.
Just my two cents.
Your point about "enjoy maintaining that pipeline" hits home. It's the same story with cloud provider APIs. They'll deprecate an entire billing dimension without warning, and suddenly your RI coverage dashboards are useless for a week.
I do think a scheduled pod is the right call for this, though. At least you can monitor its failures in one place, versus some serverless function that might fail silently. The key is building those API timeouts and retries with exponential backoff into the job from day one. You can't trust the remote service's SLA.
Every dollar counts.
Totally feel that on the latency spikes. I've found the same thing when pulling for a quarterly review - you think you can grab a whole quarter's data in one go, and then it just... hangs.
The "enjoy maintaining that pipeline" line is the real talk though. It reminds me of when we had a beautiful Tableau setup fed by the Jira API, and they changed how they calculated story points in a "minor" update. Broke every trend line. That abstraction layer idea from later in the thread feels more and more necessary, even if it's just a few wrapper functions to centralize the pain points.
How are you handling the retry logic when you get those empty 200s? Do you just skip and log, or try the same query again after a delay?
Yeah, the "thin abstraction layer" idea feels right. It's like putting all the weird API behaviors in one box so you know exactly where to kick it when it breaks. But you're spot on about not over-engineering it - I've seen teams build a whole proxy service just for this and then spend more time maintaining *that*.
For the token cache, could you just store it in memory in your pod with a timestamp check? That way it's not a separate service, but you're also not hitting auth every single pull. I'm new to this, but is the rate limit you hit on the auth endpoint itself, or on the data calls?
That curl snippet is a one-way ticket to timeout city if you run it directly from PowerBI Desktop. Always push the data pull to a separate service, like their scheduled pod. The cardinal sin is letting a user's interactive refresh trigger a full API pull.
Your point about the "improved" API is the real wisdom. I treat the OneTrust API like a third-party vendor, because functionally, it is. You need a versioned contract in your code. I pin to a specific API version in the URL path and expect every field to disappear between major releases.
Trust but verify – and audit