--- title: Advertiser dashboards at thousands of concurrent users description: Customer-facing advertiser dashboards break the assumptions data warehouses are built on: warehouses optimize for a few heavy analyst queries, not thousands of... canonical: https://daily.dev/posts/advertiser-dashboards-at-thousands-of-concurrent-users-wwrswssf0 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Advertiser dashboards at thousands of concurrent users | daily.dev og:description: Customer-facing advertiser dashboards break the assumptions data warehouses are built on: warehouses optimize for a few heavy analyst queries, not thousands of... og:url: https://daily.dev/posts/advertiser-dashboards-at-thousands-of-concurrent-users-wwrswssf0 og:image: https://api.daily.dev/og/posts/wwrswSSf0.png og:image:alt: Advertiser dashboards at thousands of concurrent users og:image:width: 1200 og:image:height: 630 og:locale: en --- [SingleStore](https://daily.dev/sources/singlestore) [Read post](https://api.daily.dev/r/wwrswSSf0) # [Advertiser dashboards at thousands of concurrent users](https://api.daily.dev/r/wwrswSSf0 "Go to post") Customer-facing advertiser dashboards break the assumptions data warehouses are built on: warehouses optimize for a few heavy analyst queries, not thousands of concurrent small ones from customers. Predictable spikes (Monday mornings, budget resets, quarterly reporting) push query latency from seconds to tens of seconds, hurting the biggest spenders at the worst time. Common fixes like caching and pre-aggregation trade latency for staleness, backward-moving metrics, and a frozen product roadmap. Concurrency costs under consumption pricing scale worse than customer growth, eroding gross margins. The proposed fix is augmenting the warehouse with a purpose-built real-time serving layer (like SingleStore) that handles concurrent, latency-sensitive dashboard queries directly from ingestion, while the warehouse remains for deep historical analysis. [#data-warehouse](/tags/data-warehouse "Check all #data-warehouse posts")[#real-time-analytics](/tags/real-time-analytics "Check all #real-time-analytics posts")[#singlestore](/tags/singlestore "Check all #singlestore posts") Aug 14•5m read time•From [singlestore.com](https://api.daily.dev/r/wwrswSSf0 "singlestore.com") [](https://api.daily.dev/r/wwrswSSf0 "Go to post") Table of contents [The limitations of the caching mitigation stack](https://api.daily.dev/r/wwrswSSf0?a=the-limitations-of-the-caching-mitigation-stack "The limitations of the caching mitigation stack")[The True Cost of Concurrency in Consumption Models](https://api.daily.dev/r/wwrswSSf0?a=the-true-cost-of-concurrency-in-consumption-models "The True Cost of Concurrency in Consumption Models")[Architectural Transformation via a Unified Serving Layer](https://api.daily.dev/r/wwrswSSf0?a=architectural-transformation-via-a-unified-serving-layer "Architectural Transformation via a Unified Serving Layer")[Evaluating the margin impact](https://api.daily.dev/r/wwrswSSf0?a=evaluating-the-margin-impact "Evaluating the margin impact") Questions this post answers why does my data warehouse slow down when many customers hit dashboards at the same time Warehouses are optimized for a small number of large, scan-heavy queries run by internal analysts, not thousands of small concurrent queries from customers. When usage spikes, such as Monday morning budget resets or quarterly reporting, query latency can jump from around two seconds to thirty seconds as queues form under the surge. Teams debugging dashboard latency under concurrency can find related architecture breakdowns on daily.dev. what are the downsides of using caching and pre-aggregated rollups to speed up analytics dashboards Caching and rollups trade latency problems for data staleness, including metrics that move backward on refresh, which damages customer trust and can resurface in billing disputes. Pre-aggregation also freezes the set of queries a product can answer efficiently, turning simple new report requests into pipeline tickets requiring sprint estimates and slowing product evolution. Engineers weighing caching tradeoffs for analytics products can track these architecture debates on daily.dev. why does gross margin shrink as a consumption-priced analytics product gains more customers Because AdTech query loads are inherently spiky, the compute capacity required scales even more aggressively than customer count, so every new customer adds concurrent strain that steepens the cost curve faster than revenue grows. This is a structural architecture problem, not a pricing problem, so renegotiating warehouse contracts only treats the symptom. Teams evaluating margin impacts of analytics infrastructure choices can follow this analysis on daily.dev. 223 Impressions Comment Bookmark Copy Share your thoughtsPost [](https://daily.dev/sources/singlestore) [SingleStore](https://daily.dev/sources/singlestore "https://daily.dev/sources/singlestore") SingleStore Blog offers insights, tutorials, and updates on SingleStore, a distributed SQL database ... 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