Accelerate Snowflake Event Tables with Zero-Copy Interactive Analytics

Observability is an important pillar of any modern workload, and the telemetry emitted by your platform or application is the key to unlocking it. Application observability on Snowflake is enabled by event tables, which store log, metric, and trace records in OpenTelemetry format. Because they capture both telemetry records emitted by both your handler code and the Snowflake platform itself, event tables are a central location to store and query telemetry at scale.

Telemetry is verbose. Even moderately sized accounts can accumulate billions of event table rows. Yet many observability queries need only a small slice: recent errors for one service, a single trace, or the last few minutes of logs. Dashboards repeat those queries as users refresh the same filters. These requests are selective, repeated, and sensitive to latency.

A standard warehouse can run these queries alongside broader analytical work. Interactive warehouses provide a compute option designed for short-running queries and high concurrency. Previously, using an interactive warehouse to query event-table data required an intermediate interactive table, adding a second copy of the telemetry and a refresh process to maintain it.

Zero-Copy Interactive Analytics removes that intermediate layer. An interactive warehouse can now query any table directly. That feature is immensely useful on its own, but it also unlocks some interesting architectural options specifically for event tables.

One Event Table, Two Query Patterns

Observability data often serves two purposes.

The first is historical analysis. A team might compare error rates across releases, aggregate resource metrics over several weeks, or examine traces during an incident review. A standard warehouse is a natural fit for this broader analytical work.

The second is operational access. A dashboard displays the latest service errors. An engineer searches for one trace. An internal tool retrieves a narrow time range for one application. These requests benefit from low latency under concurrent use, which is where an interactive warehouse fits.

Both patterns were possible before zero-copy support. The change is how much infrastructure a team needs to support them.

A Shorter Read Path

The previous architecture used a dynamically refreshed interactive table over the event table:

That design can improve query latency, but it carries an operational cost. The interactive table consumes storage, refreshes consume compute, and TARGET_LAG sets the refresh freshness target (with a 1 minute minimum). Access controls and retention decisions span two tables, and operators must monitor refresh health.

The zero-copy path is shorter:

The main gain is a simpler architecture. The event table remains the source of truth, and consumers select the compute type that matches their work. Both paths can access telemetry as it becomes available in the event table, without waiting for a second table to refresh. Table-level access controls apply to a single source, alongside the permissions required for each warehouse.

This also makes evaluation easier: teams can test an interactive warehouse against their existing event table without building a refresh pipeline first.

Good Fits

There are at least four observability patterns that are well suited to this model.

Service Health Dashboards

Dashboards often run the same selective queries at a steady cadence: recent error counts, latency signals, or logs grouped by service and severity. An interactive warehouse gives those requests a compute path designed for short-running, concurrent queries.

Trace and Request Lookup

An engineer looking up a trace, request ID, procedure invocation, or application session needs a specific record quickly. Querying the event table directly keeps that lookup on the same data used for broader investigation. Interactive warehouses copy the telemetry to local cache for fast subsequent queries.

Recent Error Search

Incident response often starts with a narrow question: what failed for this service in the last 15 minutes? That query targets a small slice of a much larger telemetry history, making it a useful candidate for Interactive Analytics. And zero-copy analytics makes it possible to spin up interactive warehouses just for the duration of the incident.

Operational APIs and Internal Tools

Internal support tools and service APIs may also expose telemetry. Their users need predictable response times under concurrent use. These tools can query the same event table used for deeper investigation, while a standard warehouse handles heavier work.

Using Interactive Properly

Zero-copy support makes the architecture simpler, but it does not mean every telemetry query should be run with an interactive warehouse.

Query Duration and Fallback

Interactive warehouses are designed for short-running queries and high concurrency, and they enforce a five-second query limit. You can configure a fallback warehouse to avoid hard query failures, but it means the queries are restarted on a standard warehouse.

A query that usually finishes quickly can use fallback to handle occasional overruns. A query that consistently exceeds five seconds likely needs tuning or may belong on a standard warehouse.

But the consumer for any given query also matters. A fallback warehouse prevents errored panels on your Grafana dashboard. But for some latency-sensitive applications, you may actually prefer a timeout over a long query response time.

Cache Behavior and Warehouse Suspension

Interactive warehouses use local storage to cache table data. Tables associated with the warehouse are proactively warmed, while other tables warm as queries access them. Suspending the warehouse clears that cache, so queries immediately after a resume will be slower while it rebuilds.

This means the aggressive suspension policies many of you may be used to with standard warehouses are often detrimental for interactive warehouses. Also, interactive warehouses have a one-hour minimum billable period and a 24-hour minimum auto-suspend interval. And each resume starts a new minimum billing period.

For a frequently used event table, associating the table with the warehouse can make cache warming more predictable. For an occasional query, on-demand warming may be enough. But as always, you need to test and see what best fits your workloads.

Conclusion

Zero-copy interactive analytics gives event tables a low-latency, high-concurrency compute path without the duplicate table, refresh compute, or TARGET_LAG required by the previous architecture. But just like interactive analytics is not a magic bullet for all your workloads, you need to be selective in exactly what it is and is not used for.

Start with a selective, frequently used workload, such as a service-health dashboard or trace lookup. Measure its latency and concurrency behavior, choose an explicit fallback policy, and account for cache warming and warehouse lifecycle costs. Use those results to decide which queries belong on each compute path.

Resources and Further Reading

• Snowflake Interactive Analytics | Snowflake User Guide
• CREATE INTERACTIVE WAREHOUSE | Snowflake SQL Reference
• Event Table overview | Snowflake Developer Guide
• Event Table columns | Snowflake Developer Guide
• Zero-Copy Analytics General Availability Announcement | Release Notes

Accelerate Snowflake Event Tables with Zero-Copy Interactive Analytics was originally published in Snowflake Builders Blog: Data Engineers, App Developers, AI, & Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.