When a data platform decides it needs to understand how systems behave— not just what data they store — something important has shifted.
With its acquisition of Observe, Snowflake is stepping into IT monitoring and observability.
On paper, it looks like a straightforward expansion. In reality, it’s a quiet admission that data platforms can’t stay neutral observers anymore.
They need context. They need behavior. They need to know what’s changing — and why.
If that sounds familiar, it should.
Observability didn’t stop at infrastructure
Observability started as an infrastructure problem. CPU graphs. Memory usage. Latency charts that looked authoritative and explained almost nothing.
Then it moved up the stack into applications: logs, traces, events. More signal, but still mostly technical.
Now it’s moving again — into the systems where real business failures happen. CRMs. Revenue pipelines. Routing logic. Pricing rules. Automations quietly stitched together over years of “just one more fix.”
Snowflake’s move makes something explicit that the industry has been dancing around for a while: data quality and system reliability aren’t separate concerns. That is, when analytics break downstream, the root cause almost always lives upstream in system behavior.
And that behavior is governed by metadata.
What this acquisition actually signals
This isn’t about Snowflake wanting prettier dashboards.
Warehouses are table stakes now. Differentiation comes from helping teams understand how their systems actually operate, not just where the data lands. Observability is Snowflake saying, "We don't just store your data — we help you reason about the systems producing it."
That’s powerful. But it also reveals a ceiling.
Observability tools are very good at telling you that something changed. They’re far less effective at telling you what that change means.
Where observability falls short
Most modern observability amounts to high-fidelity panic.
An alert fires. A dashboard spikes. A pipeline fails. Everyone scrambles to answer the same question: what touched this?
The answer doesn’t live in logs or metrics. It lives in metadata.
- Which field changed?
- Which flows depend on it?
- Which downstream automations inherited that logic?
- Which dashboards are now confidently wrong?
Without metadata clarity, observability creates noise faster than teams can resolve it. Alerts multiply. Dashboards proliferate. Confidence erodes. This is how systems drag is born — not from a lack of visibility, but from a lack of understanding.
Why this is validating for Sweep
Sweep exists because modern systems don’t usually fail loudly. They fail silently, incrementally, and all at once.
Snowflake moving into observability validates the direction of the market. Sweep focuses on the prerequisite the market keeps tripping over.
You can’t observe what you don’t understand.
You can’t automate what you can’t explain.
And you shouldn’t give AI agents permission to act inside systems whose metadata is undocumented, drifting, or tightly coupled in ways no one can see.
Sweep treats metadata as operational truth — not as labels or documentation afterthoughts. It’s the layer that makes observability actionable instead of overwhelming, and automation safe instead of reckless.
What this means for RevOps and GTM teams
If you run Salesforce, you’re already operating a distributed system. You just weren’t given the language — or the tooling — to see it that way.
AI agents are now being dropped into GTM stacks with promises of speed. When they fail, it’s rarely because the AI is “bad.” It’s because the system underneath is incoherent.
Field definitions drift. Routing logic changes without visibility. Automations stack on top of forgotten assumptions.
Data-layer observability won’t catch that in time. Metadata observability will.
The question that actually matters
The real question isn’t whether your stack has observability.
It’s whether you understand your system well enough to trust it.
Snowflake’s move confirms what we’ve believed for a long time: the future belongs to teams who can see, explain, and govern how their systems work— before they automate them.
That future runs on metadata.