Cribl pitching a "real alternative" to a direct data warehouse? Doubt it. This partnership just moves data around. More pipelines, more cost.
Key questions for anyone considering this:
* Where is the data processed? Cribl Stream workers in your cloud? That's your compute bill.
* What's the egress from Cribl to Snowflake? That's a network cost.
* Are you paying for Cribl's processing *and* Snowflake's storage/compute? Now you have two vendors to manage and pay.
This isn't an alternative. It's a middleman. You're adding a layer that will increase complexity and cost. Show me the TCO comparison.
show me the bill
> This isn't an alternative. It's a middleman.
Funny, that's exactly why it might be useful for some shops. You're assuming a "direct" pipeline is inherently cleaner, but I've seen enough teams get burned by vendor-locked transformation logic baked right into their Snowflake streams. When you need to re-route that data flow or apply a different set of PII rules for another destination, you're stuck.
Cribl as a middleman centralizes that routing logic outside the warehouse. Yes, it's another piece and another bill. But calling it just a cost layer ignores the operational tax of managing transformation sprawl across half a dozen "direct" integrations that all do it differently. Sometimes a controlled pinch point beats a dozen direct lines.
If your argument is purely about TCO, fine. But complexity isn't just about number of vendors, it's about the number of moving parts you have to rewire every time a requirement changes.
So the "operational tax" argument. I get it, transformation sprawl is a real thing, I've seen teams duct-taping five different enrichment scripts together. But you're swapping one type of tax for another. A controlled pinch point is still a pinch point, and if Cribl goes down or misroutes a field, you're not just debugging one pipeline, you're debugging the one pipeline that all your data is funneling through.
What happens when you need to reprocess last week's data with a new PII rule? With direct Snowflake pipelines you can usually replay in place. With Cribl in the middle, are you re-streaming everything through their workers? That's a double compute hit, plus Snowflake storage for the original load.
I'd be more interested in a real case study showing latency and cost numbers under load, not just architecture diagrams of how tidy it all looks. Has anyone actually benchmarked this against a well-structured dbt pipeline?
cg