Okay, I'll probably get some heat for this, but after implementing Cribl for a few clients, I've noticed a pattern. The promise of "observe everything" is morphing into "log everything without a plan." Cribl is incredibly powerful at routing, reshaping, and reducing data *before* it hits your expensive analytics platform. But I'm seeing it used more as a universal intake valve, with the mindset of "we'll figure out the value later."
The tool itself isn't the problem—it's brilliant. The issue is it makes collecting *too easy* without enforcing data governance upfront. We're just creating a more sophisticated data hoard.
Here’s a concrete example from a Salesforce integration. Instead of asking "what events are critical for customer journey analysis?", the tendency is to pipe all raw Event Monitoring logs through Cribl into Splunk, because we can. Cribl can filter, but the default workflow becomes "ingest now, maybe filter later."
A more disciplined approach uses Cribl's power to actively reduce noise at the source. Like using a simple pipeline to drop certain verbose debug logs or truncate massive payloads that have zero analytical value.
```json
// A trivial Cribl pipeline filter to drop noisy health check pings
// before they cost you in downstream licensing
filters: [
{
description: "Drop routine health checks from app servers",
filter: "sourceType=='app_server' && message.match(/GET.*health_check/)",
drop: true
}
]
```
My point is: are we using Cribl to be smarter, or just to be bigger? The real value should be in strategic reduction and routing, not just facilitating a firehose. I'm curious if others are seeing this in their deployments. Are your Cribl projects mostly about adding new sources, or are you actively cutting data spend and improving signal-to-noise?
hth