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A Snowflake alert fires at 7:42 a.m.
A warehouse query is running slower than usual. Credit consumption has spiked and a table refresh missed its SLA.
For most data teams, this is familiar territory. Snowflake’s native observability surfaces the signal quickly and accurately. You know what changed and where it happened inside the warehouse. The question that follows is harder and more consequential:
Now what?
Which dashboards are affected? Which teams are relying on this data right now? Is this a minor technical anomaly or a material business risk that needs immediate action?
That moment - between detection and decision - is where many modern data teams still struggle.
Snowflake’s Observability Philosophy: Strong by Design
Snowflake has taken a deliberate and pragmatic approach to observability. By embedding monitoring directly into the data warehouse, it gives teams deep visibility into warehouse health and performance: query behavior, resource utilization, storage, costs, and execution patterns.
For Snowflake-centric workloads, this is a strong foundation. Engineers can quickly diagnose performance regressions, optimize workloads, and keep the warehouse running efficiently at scale. As data platforms grow more complex, this kind of native instrumentation is not optional, it’s essential.
But Snowflake’s observability model reflects its scope: the warehouse itself.
As organizations mature, that scope becomes a limiting factor.
The Business Context Gap
Modern data teams are no longer responsible only for running queries efficiently. They are accountable for the data products that shape decisions: executive dashboards, revenue metrics, forecasting models, machine-learning pipelines, and operational reports.
A slow query is rarely the real problem. The real question is whether that issue affects a metric the CFO reviews at 9 a.m., a churn model retrained overnight, or a dashboard used by hundreds of operators across the business.
Native observability answers what broke.
Data trust requires understanding why it matters.
This is the gap many Snowflake customers encounter as they scale: strong technical monitoring, but limited visibility into business impact and downstream consequences.
Limitation #1: Observability Stops at the Warehouse Boundary
Snowflake can only observe what happens inside Snowflake.
In practice, critical data workflows span far beyond the warehouse. Data is ingested from multiple sources, transformed in orchestration tools, modeled in transformation layers, and consumed in BI platforms, reverse ETL tools, and ML systems.
When an issue originates upstream—or propagates downstream—warehouse-level signals alone are often insufficient. Teams are left manually piecing together lineage across tools to understand where a problem started and how far it spread.
This isn’t a flaw in Snowflake. It’s a reflection of architectural reality: modern data stacks are inherently multi-platform.
Sophisticated teams increasingly layer cross-platform observability on top of Snowflake to see the entire lifecycle of their data, not just the point where it lands.
(This is a natural place to introduce how Sifflet connects Snowflake metadata with upstream and downstream systems.)
Limitation #2: Technical Signals Without Business Meaning
Snowflake tells you when freshness thresholds are missed or when workloads behave abnormally. What it cannot tell you—by design—is whether those anomalies affect high-value business outcomes.
Not all data issues are equal. A delayed table powering an unused report is very different from one feeding revenue recognition or executive KPIs.
Without business context—ownership, usage, criticality - teams are forced to treat all alerts as equally urgent. The result is alert fatigue, slower response times, and misaligned priorities between data teams and stakeholders.
Mature organizations are moving toward observability models that rank issues by business impact, not just technical severity—so engineers can focus first on what truly matters.
(This is where you can reference Sifflet’s ability to enrich alerts with usage, ownership, and business relevance.)
Limitation #3: No Downstream Impact Visibility
Perhaps the most challenging gap is downstream impact.
When something breaks in Snowflake, the consequences rarely stay there. The issue may surface hours later in a BI tool, distort a machine-learning model, or quietly erode trust in a dashboard long after the root cause occurred.
Snowflake’s native tooling has no way to show how warehouse events ripple outward into decision-making layers. That visibility is critical for root cause analysis—and for rebuilding trust once something goes wrong.
Without it, teams spend hours answering questions after the fact: Who saw this? What decisions were affected? How do we prevent this next time?
End-to-end lineage and impact analysis are becoming table stakes for organizations that treat data as a shared business asset rather than an internal engineering concern.
Observability as a Maturity Curve, Not a Replacement
For Snowflake customers, the takeaway is not that native observability is insufficient—it’s that it is foundational.
Snowflake provides the technical truth of the warehouse. What many teams add next is a complementary layer that connects that technical truth to business reality: cross-platform lineage, impact-aware alerting, and shared context across roles.
This evolution reflects a broader shift in how data teams define success. Reliability is no longer just about uptime and performance. It’s about confidence—confidence that the data driving decisions is accurate, timely, and understood.
From Detection to Decision
The future of data observability is not more alerts. It’s better decisions.
When teams can see not only that something broke, but who it affects, what it impacts, and what action to take, observability becomes a bridge between data infrastructure and business outcomes.
That’s the gap many Snowflake customers are now closing—by pairing native warehouse observability with a business-aware data observability layer designed to complement it.
If you’re exploring how to extend Snowflake’s strengths into end-to-end data trust, this is a conversation worth having.
Learn how Snowflake and Sifflet work together to help teams move from detection to decision.
Request a demo to see what business-aware observability looks like in practice.
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