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In the last 30 minutes, look through our CDN logs for elevated 5xx responses and group them by host, path, and status code.

Compare CDN response-time metrics for the last hour against the previous hour and identify the hosts or paths with the largest regression.

Show me all error-level logs from the payments service in the last 15 minutes and group them by error message.

Find any requests with response times over 2 seconds in the past hour and break them down by endpoint.

Are there any new error patterns in our application logs over the last 24 hours that weren't present yesterday?

Which datasets contain production CDN logs for our edge or delivery layer?

List all datasets owned by the platform team along with their tags and ingestion volume.

Show me every dataset in the staging environment and what fields they contain.

Which datasets have a `customer_id` field, and how many distinct values does it have?

Find the dataset that contains our Kubernetes audit logs and show me its schema.

Which datasets ingested the most data in the last 7 days, and how does that compare to the previous week?

Show me the top 10 services by log volume over the last 24 hours.

Which fields contribute most to our ingestion volume — are there any candidates for exclusion filters?

Break down ingestion by team using the `team` tag for the last 30 days.

Search our CDN logs for cache-related fields and show whether cache miss rates increased in the last 24 hours.

Identify the top 20 paths driving origin fetches over the last 6 hours.

Which assets had the lowest cache hit ratio yesterday compared to last week?

Use CDN logs from the last 15 minutes to find unusual request-volume spikes by client IP, user agent, host, and path.

Find any IPs making more than 100 requests per minute in the last hour and show what they're hitting.

Are there any suspicious user-agent patterns in our access logs over the last 24 hours?

Show authentication failures grouped by source IP and account over the last 6 hours.

Plot a time series of error rates per service for the last 24 hours, bucketed every 5 minutes.

Show me the p50, p95, and p99 latency for the checkout endpoint over the past day.

Compare request volume this hour against the same hour last week and highlight services with significant deviation.