{"templateName":"blog-page","cssClassNames":"blog-page page basicpage summit-page","allowedRenditionsWidth":["320","480","640","768","960","1200","1440","1920"],"description":"Discover how Whatnot used observability, AI analytics and real-time monitoring in Snowflake to support hyper-growth and faster decisions.","language":"en","title":"Observability at Scale: Whatnot at Snowflake Summit","analyticsPageType":"homepage","analyticsCategory":"general","analyticsSubCategory":"","excludeFromAnalytics":false,":mappedPath":"/en/blog/observability-at-scale-whatnot-snowflake-summit/",":type":"snowflake-site/components/structure/page",":items":{"root":{"columnCount":12,"columnClassNames":{"experiencefragment-banner":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-sub-header":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-pre-footer":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-header":"aem-GridColumn 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Fornaca"}},{"authorImage":{"id":"image-2bd8fd9236","height":"558","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--0ae9fbe7-cad5-4e51-8684-71c9e2629fd4/evan-hou.png?quality=85&preferwebp=true","alt":"Evan Hou","lazyEnabled":true,"width":"558",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-24ead65e5e","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/evan-hou/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Evan Hou"}},{"authorImage":{"id":"image-5e4de0456e","height":"288","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--5fc24974-03b5-41b8-a8c3-162312c1fa04/stephen-bailey.jpg?quality=85&preferwebp=true","alt":"Stephen 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Venkatesan"}}],"image":{"id":"image-73b36230f7","height":"720","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--60326bec-a162-481c-bb49-069a3c02d588/blog-snowflake-whatnot-1680x720.png?quality=85&preferwebp=true","alt":"Snowflake x Whatnot","lazyEnabled":true,"width":"1680",":type":"snowflake-site/components/image"},"timeToRead":"8","publicationDate":"JUL 27, 2026","tag":{"tagText":"Retail and Consumer Goods","tagColor":"#29B5E8"},"title":{"lines":["Snowflake Summit 2026: How Whatnot Turned Hyper-Growth Data into Clear Business Insights"],"type":"heading2",":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/blog/blog-hero"}},":itemsOrder":["blog_hero"]},"responsivegrid_content":{"columnCount":12,"columnClassNames":{"blog_text":"aem-GridColumn aem-GridColumn--default--12","premium_content_bann":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text":{"id":"blog-text-317835c6b5","text":"\u003Cp\u003EHave you ever wondered what is \u003Ci\u003Eactually\u003C/i\u003E happening inside a massive data platform at any given second?\u003C/p\u003E\r\n\u003Cp\u003EFor many companies, data infrastructure operates like a black box. Millions of data points go in, complex queries run and reports come out. But when things slow down, costs spike or a critical dashboard goes blank, finding the root cause can feel like floundering in the dark.\u003C/p\u003E\r\n\u003Cp\u003ETo shed light on this challenge, the live-shopping platform \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://www.whatnot.com/?srsltid=AfmBOopUWw3FqUqWP6zSjpnGqcIzDQ94_MH9Ek4XaMkxM_i89-_icEol\"\u003EWhatnot\u003C/a\u003E joined Snowflake on stage at \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://reg.snowflake.com/flow/snowflake/summit26/sessions/page/catalog/session/1768514412651001NaiB\"\u003ESnowflake Summit 2026\u003C/a\u003E. They outlined a new blueprint for the modern enterprise: one that combines a legendary hyper-growth story with automated AI analysts and crystal-clear platform monitoring. This partnership demonstrates how modern data tools keep customer experiences smooth, reliable and completely visible, even under massive, real-time demand.\u003C/p\u003E\r\n\u003Ch2\u003EThe reality of hyper-growth: billions of events, zero room for error\u003C/h2\u003E\r\n\u003Cp\u003EWhatnot has captured international momentum as one of the fastest-growing marketplaces ever, pacing ahead of the historic trajectories of legacy ecommerce giants such as eBay and Amazon within its first few years. Today, it stands as the premier live-shopping platform across North America, the UK, Australia and Europe.\u003C/p\u003E\r\n\u003Cp\u003EThese metrics underscore the sheer scale of their operation and the immense volume handled by their data infrastructure:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E$8 billion generated\u003C/b\u003E in live global gross merchandise volume (GMV) in 2025\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E20M+ new accounts\u003C/b\u003E added across all markets in 2025\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EA 285% year-over-year increase\u003C/b\u003E in first-time buyers in 2025\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EOver 550,000 hours of livestreams\u003C/b\u003E hosted every single week — with active viewers averaging \u003Cb\u003E95+ minutes a day\u003C/b\u003E on the app\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EHosted the largest live shopping stream in U.S. history\u003C/b\u003E in 2026 that had \u003Cb\u003E583,000 concurrent viewers\u003C/b\u003E and \u003Cb\u003E555,000 users\u003C/b\u003E entering the same giveaway at the same time\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EBehind the scenes, auction bids, chat messages and transactions generate billions of data points daily. All that data converges inside Snowflake to power the immediate user experience.\u003C/p\u003E\r\n\u003Cp\u003E"Data isn't just for historical reports at Whatnot — it drives the live app experience," explains Alice Leach, Engineering Manager at Whatnot. "If a customer buys an item on a livestream, our machine learning algorithms need to recommend related products within minutes, not days. If we experience a data delay, it directly impacts our buyers and sellers."\u003C/p\u003E\r\n\u003Cp\u003EOriginally, Whatnot managed all this information through a single, centralized data team using dbt. But as the company exploded, this setup became a major bottleneck. To fix it, Whatnot shifted to a modular data stack. Using infrastructure as code (IaC), individual business units (such as fraud prevention or vendor analytics) were given the independence to spin up their own dedicated Snowflake warehouses on demand and manage their own pipelines.\u003C/p\u003E\r\n\u003Cp\u003EDecentralization cleared organizational blockages, but it created a new puzzle: How do you give teams complete freedom to run their own data pipelines while maintaining total visibility, cost control and performance quality across the entire company?\u003C/p\u003E\r\n\u003Ch2\u003EThe AI solution: moving from data requests to conversational analytics\u003C/h2\u003E\r\n\u003Cp\u003EWhile decentralizing the infrastructure helped the engineering side, it highlighted a human bottleneck: data scientists. As business leaders rushed to make fast, day-to-day decisions, data scientists became trapped in an endless loop of answering ad hoc data questions over Slack.\u003C/p\u003E\r\n\u003Cp\u003ETo move "uncomfortably fast," Whatnot realized it needed to lower the barrier to entry so anyone could access data at the speed of typing.\u003C/p\u003E\r\n\u003Ch3\u003EThe evolution of the virtual analyst\u003C/h3\u003E\r\n\u003Cp\u003EWhatnot’s journey to scale analytics evolved through three phases:\u003C/p\u003E\r\n\u003Col\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E2024 (the rigid Slackbot):\u003C/b\u003E Whatnot built an AI Slack bot (@databot) to auto-generate SQL queries. While it could handle simple requests, it required heavy maintenance and continuous human verification.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E2025 (decentralized tools):\u003C/b\u003E The company integrated \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/blog/engineering/native-semantic-views-ai-bi/\"\u003ESnowflake semantic views\u003C/a\u003E across multipurpose apps such as Sigma and Glean. By pairing Snowflake with a leading, advanced LLM, text-to-SQL accuracy crossed 90% in Whatnot’s internal testing, but the ecosystem lacked a cohesive "front door."\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E2026 (the agentic analytics era):\u003C/b\u003E Whatnot rolled out Hex Threads — a custom data companion powered by \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/developers/guides/getting-started-with-cortex-agents/\"\u003ESnowflake Cortex Agents\u003C/a\u003E. Instead of forcing users to know exactly where a database table lives or how to debug a SQL error, the AI safely scans the data network to act as a conversational assistant.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ol\u003E\r\n\u003Ch3\u003EThe business impact\u003C/h3\u003E\r\n\u003Cp\u003EThe transition from clunky, manual data pulls to agentic AI completely transformed the company's internal culture:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EWidespread adoption:\u003C/b\u003E Within 90 days of launch, \u003Cb\u003Eover 80% of Whatnot’s 1,000+ employees\u003C/b\u003E were actively using the agentic solution.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EUniversal access:\u003C/b\u003E 17 different company departments reached \u003Cb\u003E100% active utilization\u003C/b\u003E. Teams such as performance marketing, talent acquisition and business operations became entirely self-sufficient.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EDeeper strategic analysis:\u003C/b\u003E Rather than just pulling static lists, teams are using conversational AI for complex work — such as tracking international weekly trends, matching messy data fields and building seller churn prediction models.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ERapid iteration on business and product analysis:\u003C/b\u003E What previously required ad hoc data requests planned weeks in advance has been compressed into data available at the speed of typing. This drastically reduces technical friction, allowing product teams to run SQL execution checks, test feed-generation logic and rapidly iterate on predictive models (such as seller churn) without waiting on engineering queues.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ctable\u003E\r\n\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"2\"\u003E\u003Cp\u003E\u003Cb\u003EThe Data Whatnot Employees Are Asking Agents to Retrieve\u003C/b\u003E\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EBusiness topic\u003C/b\u003E\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EShare of total queries\u003C/b\u003E\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003ESeller data\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003E16.0%\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003ELivestreams\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003E11.9%\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EOrders\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003E11.4%\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003EData engineering\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003E8.2%\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n\u003Cp\u003E\u003Cbr\u003E\r\nWhatnot is currently bringing this exact capability to the public. Through the Whatnot Seller Hub, livestreamers operate as independent business executives. By embedding Cortex Agents with strict row-level security (designed to ensure data remains private and protected), sellers can naturally text the app for instant updates, such as: "\u003Ci\u003EShow me my top buyers in the last 30 days\u003C/i\u003E."\u003C/p\u003E\r\n\u003Ch2\u003ETechnical infrastructure: fast, affordable and democratic monitoring\u003C/h2\u003E\r\n\u003Cp\u003EGiving hundreds of internal employees and thousands of external sellers the freedom to query data and spin up warehouses introduces a unique operational challenge: managing the risk of runaway costs and unexpected platform errors.\u003C/p\u003E\r\n\u003Cp\u003ETo help balance the rapid speed of decentralized AI, Whatnot's ongoing goal has been to cultivate a monitoring setup designed to be fast, cost-effective and easy to read.\u003C/p\u003E\r\n\u003Ch3\u003EBreaking the logging bottleneck\u003C/h3\u003E\r\n\u003Cp\u003EHistorically, tracking platform health in real time was a massive headache. Standard utilization logs were delayed by three to four hours — far too slow to catch a live pipeline error. The alternative was running massive diagnostic scans every 15 minutes, which burned through the compute budget and required strict admin security clearances just to view the logs.\u003C/p\u003E\r\n\u003Cp\u003ETo fix this, Snowflake overhauled its native telemetry engine using next-generation \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/developer-guide/logging-tracing/event-table-setting-up\"\u003Eevent tables\u003C/a\u003E via \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/product/features/snowflake-trail/\"\u003ESnowflake Trail\u003C/a\u003E. This update made event ingestion 10x faster, removing the financial stress of comprehensive logging.\u003C/p\u003E\r\n\u003Ch3\u003EMoving from complex code to natural language alerts\u003C/h3\u003E\r\n\u003Cp\u003ESetting up high-quality system alerts used to require data engineers to write over a hundred lines of intricate SQL code. This technical barrier effectively locked business analysts and operational managers out of the loop.\u003C/p\u003E\r\n\u003Cp\u003ENow, using Snowflake's AI-assisted observability workflows, users can stand up automated infrastructure monitoring simply by typing a conversational request into CoCo, in \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight\"\u003ESnowsight\u003C/a\u003E UI, such as:\u003C/p\u003E\r\n\u003Cp\u003E"\u003Ci\u003ECreate an alert that detects when our warehouses experience performance anomalies or sudden cost spikes, and email me the summary daily\u003C/i\u003E."\u003C/p\u003E\r\n\u003Cp\u003EBehind the scenes, the built-in AI is designed to evaluate the user's business intent, scan the relevant platform views, build the underlying code logic and provision the notification channel automatically.\u003C/p\u003E\r\n\u003Ch2\u003EThe road ahead: epistemic hygiene and proactive data operations\u003C/h2\u003E\r\n\u003Cp\u003EAs Whatnot looks toward the future, the ultimate goal is to transition from diagnosing past problems to actively preventing them.\u003C/p\u003E\r\n\u003Cp\u003EHowever, scaling AI analytics has taught the company an important lesson: Dropping technical friction inevitably exposes organizational friction. When data moves at the speed of typing, gaps in data modeling, ownership and metric definitions become very obvious very quickly. Furthermore, decentralized AI systems can suffer from "agent sprawl" if left completely unguided.\u003C/p\u003E\r\n\u003Cp\u003ETo keep its data trustworthy, Whatnot enforces a strict playbook of epistemic hygiene and agent guidance across its AI network:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EProbabilistic language\u003C/b\u003E: Agents must use cautious, probabilistic language by default ("\u003Ci\u003Ethis likely reflects\u003C/i\u003E," "\u003Ci\u003Ethe data is consistent with\u003C/i\u003E"), and are explicitly banned from using definitive phrases such as "\u003Ci\u003Ethis proves\u003C/i\u003E" or "\u003Ci\u003Ethis clearly shows\u003C/i\u003E."\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EObservation vs. interpretation\u003C/b\u003E: The AI must explicitly separate raw data facts from subjective business interpretations.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ENo blind causation\u003C/b\u003E: Agents are strictly prohibited from declaring causation from simple observational data, defaulting to terms such as "\u003Ci\u003Eassociated with\u003C/i\u003E" or "\u003Ci\u003Ecorrelates with\u003C/i\u003E."\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EBy combining these rigorous structural guardrails with Snowflake’s upcoming features — such as Unified Observability Hubs for centralized cost tracking and AI-driven smart alert recommendations — the line between software engineers, data scientists and business managers is intentionally blurring.\u003C/p\u003E\r\n\u003Cp\u003EBy embracing modular data stacks, affordable real-time event logs and conversational AI, companies like Whatnot are turning the infrastructure black box into a major competitive advantage — ensuring data teams spend less time writing custom logging scripts, and live shoppers enjoy a flawless, real-time experience.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003EAll statistics are provided by Whatnot internal analytics, as of July 27, 2026.\u003C/i\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003E\u003Cb\u003EForward Looking Statements\u003C/b\u003E\u003C/i\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003EThis article contains forward-looking statements, including about our future product offerings, and are not commitments to deliver any product offerings. Actual results and offerings may differ and are subject to known and unknown risk and uncertainties. See our latest 10-Q for more information.\u003Cbr\u003E\r\n \u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"premium_content_bann":{"id":"premium-content-banner-6edd52a431","contentHeadline":"AI Data Cloud for Retail and Consumer Goods","contentDescription":"Learn how to grow your business and deliver meaningful consumer experiences while lowering costs with an agile supply chain and efficient operations.","showImage":false,"buttons_container":{"layout":"SIMPLE","id":"container-1446b65cb5",":type":"snowflake-site/components/button/buttons-container",":items":{"button":{"id":"button-7dbc5c9d55","showOutboundIcon":true,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/solutions/industries/retail-consumer-goods/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","appliedCssClassNames":"snowflake-button-primary snowflake-button-blue 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