Observability at Scale: Whatnot at Snowflake Summit

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Blog/ Retail and Consumer Goods/Snowflake Summit 2026: How Whatnot Turned Hyper-Growth Data into Clear Business Insights

Jul 27, 2026/8 min readRetail and Consumer Goods

Snowflake Summit 2026: How Whatnot Turned Hyper-Growth Data into Clear Business Insights


!Snowflake x Whatnot

Have you ever wondered what is _actually_ happening inside a massive data platform at any given second?

For 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.

To shed light on this challenge, the live-shopping platform Whatnot joined Snowflake on stage at Snowflake Summit 2026. 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.

The reality of hyper-growth: billions of events, zero room for error

Whatnot 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.

These metrics underscore the sheer scale of their operation and the immense volume handled by their data infrastructure:

• $8 billion generated in live global gross merchandise volume (GMV) in 2025

• 20M+ new accounts added across all markets in 2025

• A 285% year-over-year increase in first-time buyers in 2025

• Over 550,000 hours of livestreams hosted every single week — with active viewers averaging 95+ minutes a day on the app

• Hosted the largest live shopping stream in U.S. history in 2026 that had 583,000 concurrent viewers and 555,000 users entering the same giveaway at the same time

Behind 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.

"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."

Originally, 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.

Decentralization 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?

The AI solution: moving from data requests to conversational analytics

While 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.

To move "uncomfortably fast," Whatnot realized it needed to lower the barrier to entry so anyone could access data at the speed of typing.

The evolution of the virtual analyst

Whatnot’s journey to scale analytics evolved through three phases:

1. 2024 (the rigid Slackbot): 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.

2. 2025 (decentralized tools): The company integrated Snowflake semantic views 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."

3. 2026 (the agentic analytics era): Whatnot rolled out Hex Threads — a custom data companion powered by Snowflake Cortex Agents. 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.

The business impact

The transition from clunky, manual data pulls to agentic AI completely transformed the company's internal culture:

• Widespread adoption: Within 90 days of launch, over 80% of Whatnot’s 1,000+ employees were actively using the agentic solution.

• Universal access: 17 different company departments reached 100% active utilization. Teams such as performance marketing, talent acquisition and business operations became entirely self-sufficient.

• Deeper strategic analysis: Rather than just pulling static lists, teams are using conversational AI for complex work — such as tracking international weekly trends, matching messy data fields...