Advertiser dashboards at thousands of concurrent users

Digital advertising reporting dashboards require rapid, concurrent access to real-time data for thousands of customers, but they are commonly built on data warehouses designed for occasional, large-scale internal analytics queries. Predictable activity spikes, such as Monday budget resets and reporting cycles, can overwhelm warehouse capacity, causing queues, slow responses, and rising consumption costs that erode margins as customer adoption grows. Common mitigations including BI extracts, caches, and pre-aggregated tables can reduce latency but introduce stale or inconsistent metrics, restrict product flexibility, and increase engineering maintenance. The proposed approach is to supplement—not replace—the warehouse with a real-time serving layer such as SingleStore, which is designed to handle high volumes of small, filtered aggregation queries with low latency while data is freshly ingested. Under this model, warehouses remain suited to deep historical analysis and exploratory workloads, while the serving layer supports customer-facing dashboards and potentially growing demand from AI models and agents.

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