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Behind the Booth: How We Delivered a Live AI Experience at Snowflake Summit

Behind the Booth: How We Delivered a Live AI Experience at Snowflake Summit

Written by

Ryan Lewis, Sr. Lead Consultant

Published

June 13, 2025

AI & Machine Learning
Snowflake
Sigma

We wanted to do more than just hand out stickers at this year’s Snowflake Summit. We wanted to create an experience—something fun, immersive, and hands-on that brought the power of AI, Snowflake, and real-time analytics to life.

So we built a two-part AI solution that combined facial recognition, mood detection, and instant dashboarding. The result? A booth where attendees could get a custom AI-generated caricature and learn how advanced tools like Snowflake, Landing AI, and Sigma come together to power intelligent experiences.

The Big Idea: Fun Meets Function

We set out to show what’s possible when modern data and AI tools are thoughtfully combined. Our booth experience worked like this:

1

Snap a Photo
Attendees had their photo taken at the booth.

2

Generate a Caricature

The image was sent to an AI service to create a playful caricature.

3

Analyze Your Mood

At the same time, the photo was sent to a custom mood detection model powered by LandingLens (via Snowflake) to classify your expression.

4

See the Results in Real Time

Your mood data was logged in Snowflake and instantly visualized on a Sigma dashboard alongside aggregated visitor insights.

5

Take Home the Fun

Every participant got a printed caricature to keep—and a great story to share on LinkedIn.

Behind the Scenes: How It All Worked

Data Collection & Processing

We started by gathering internal data—photos of the OneSix team acting out specific moods. These images were passed through AWS Rekognition to crop the headshots and stored in an S3 bucket.

But to train a meaningful model, we needed more. We augmented our dataset with hand-picked stock photos and even generated synthetic headshots using GPT-image-1 to ensure variety and balance across moods.

Model Training in LandingLens

Training our mood detection model involved:

  • Creating a LandingLens project through the Snowflake Native App.
  • Uploading training data by mood for automatic labeling.
  • Launching training runs with custom settings for split ratios, epochs, and image augmentation.

LandingLens made it easy to go from raw images to a trained model with its low-code interface and tight Snowflake integration.

Real-Time Inference & Dashboarding

When an attendee took a photo:

  • The image was cropped via AWS Rekognition.
  • The cropped headshot was sent to the LandingLens model endpoint.
  • The predicted mood was written to Snowflake.
  • Sigma read directly from Snowflake to update our real-time dashboard—no lag, no duplication.

The entire process happened in seconds. One moment you’re smiling at the camera, the next you’re a dot on a live mood chart.

Why We Chose These Tools

Snowflake Icon (2)

Snowflake

  • Unified data platform for storage, compute, and ML integrations
  • Central hub for all interactions—from inference to visualization
landingai-icon

Landing AI's LandingLens

  • Ideal for small, focused datasets
  • Snowflake Native App keeps data and compute governed and secure
  • Streamlines model development with a no-code interface
  • Supports custom training and inference at scale
sigma (2)

Sigma

  • Real-time dashboarding directly from Snowflake
  • No data extraction = stronger security and compliance
  • Fast to build, easy to customize, visually compelling

The Takeaway

This wasn’t just a gimmick. It was a live demonstration of what’s possible when you combine the right technologies with a little creativity.

At OneSix, we believe data and AI should feel approachable and practical. This booth experience proved that smart, scalable, and governed AI applications don’t have to be intimidating—or boring. Thanks to everyone who stopped by to participate. We hope you had as much fun as we did. See you next year!

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Ditch the Data Bottlenecks: Why Sigma Wins Over Looker

Ditch the Data Bottlenecks: Why Sigma Wins Over Looker

Written by

Mike Galvin, Managing Director

Published

April 22, 2025

Data Analytics
Sigma

Data modeling is essential for turning raw data into actionable insight—but not all modeling approaches are built to last. Looker, with its powerful semantic layer and LookML modeling language, once set the standard for centralized data governance and self-service analytics. But as many organizations have learned the hard way, what starts as a promising framework can become a long-term liability.

Despite its early appeal, Looker’s modeling approach often creates friction, complexity, and bottlenecks that hinder agility and scalability over time. Fortunately, newer tools like Sigma offer a fresh approach—one that eliminates the overhead of rigid data models while empowering business users to explore and analyze with freedom and confidence.

The Promise of Looker’s Data Modeling Layer

Looker introduced a compelling vision for modern business intelligence (BI): a centralized, code-based semantic layer that abstracts SQL logic and standardizes metrics across the organization. Using LookML, data teams can define dimensions, measures, joins, and business logic in reusable files. The benefits are clear—centralized governance, version control, and consistency.

This model appealed to many organizations looking to bring order to fragmented reporting environments and reduce the burden on analysts manually writing SQL. For a time, it worked well. But as teams scaled, so did the problems.

Why Looker Models Struggle Over Time

1. Complexity Grows Rapidly

As data needs evolve, LookML models tend to grow in size and complexity. What starts as a manageable set of views and explores can balloon into a tangled web of dependencies, nested joins, and abstracted logic that’s hard to debug, let alone maintain. Small changes can have unintended ripple effects, making even simple updates risky.

2. Bottlenecks for Business Users

The centralized modeling layer puts the power squarely in the hands of the data team. While this ensures consistency, it also creates a bottleneck: business users must request updates or new metrics through developers. This slows down decision-making and discourages innovation. Over time, users stop exploring and start waiting.

3. Skills Gap and Maintenance Burden

LookML is a proprietary language, and maintaining models requires not just SQL knowledge, but fluency in YAML, Git workflows, and Looker’s specific abstractions. Training new team members or scaling your analytics function becomes a challenge—especially when compared to more intuitive tools.

4. Limited Real-Time Collaboration

Looker is largely dashboard-driven, with limited capabilities for spontaneous collaboration or iterative exploration. Business users can slice data only within the bounds defined by the model, which limits their ability to answer new questions on the fly or experiment with different views of the data.

5. Governance vs. Agility Tradeoff

Looker’s emphasis on governance often comes at the cost of agility. The more tightly you lock down models to ensure data quality, the more you constrain users’ ability to explore. In many organizations, this leads to shadow BI, where teams export data to Excel or create parallel solutions outside the sanctioned platform—defeating the very purpose of central governance.

Why Sigma Is Better

1. Spreadsheet-Native Interface

Sigma brings the power of a modern data warehouse to a familiar spreadsheet interface. Business users don’t need to learn LookML or write SQL to get insights. They can build pivot tables, apply formulas, and create visualizations just like they would in Excel—only at cloud scale.

“Users interact with data using a spreadsheet-like UI that requires no coding—lowering the barrier to entry for insights.”
Source: 10 Best Alternatives to Looker in 2025

2. Real-Time Access Without Predefined Models

Unlike Looker, Sigma allows direct querying of cloud data platforms without the need for a semantic layer. Users explore data in real time, dramatically reducing time-to-insight and avoiding long request queues for model updates.

3. True Business User Empowerment

Sigma breaks down the wall between technical and non-technical users. Analysts and business users can create calculations, join datasets, and build dashboards without writing code—reducing the load on data engineers and accelerating time to insight. By enabling users to filter, join, calculate, and visualize data independently, Sigma reduces dependency on technical teams.

“Everyone from analysts to execs can make data-driven decisions without waiting for developers.”
Source: 10 Best Alternatives to Looker in 2025

4. Governed Flexibility

Governance isn’t an afterthought in Sigma—it’s built into the platform. Admins can define access controls, certified datasets, and permissions to ensure data integrity while still giving users room to explore. It’s the best of both worlds: flexibility with oversight.

5. Seamless Embedded Analytics

Sigma makes it easy to embed business intelligence (live dashboards, tables, and interactive analytics) directly into your customer-facing applications, portals, or internal tools. With secure, customizable embeds that respect user permissions, teams can deliver real-time insights to customers, partners, or business units—without duplicating infrastructure or exposing raw data.

6. Built for the Modern Data Stack

Sigma’s cloud-native architecture works seamlessly with Snowflake, Databricks, BigQuery, and dbt. By shifting compute to the data warehouse and integrating with upstream transformation tools, Sigma eliminates the need for a rigid, proprietary modeling layer.

From Rigid Models to Real-Time Insights

Looker helped pioneer the idea of a governed semantic layer, but its rigid modeling approach often becomes a barrier rather than a bridge. The overhead, complexity, and user friction it introduces make it difficult to sustain in the long run—especially for organizations that need speed, flexibility, and scale.

Sigma offers a smarter path forward: one that empowers business users, accelerates insight, and aligns with the architecture of the modern data stack. If your Looker models are holding you back, it might be time to explore a better way.

Accelerate Analytics
with Sigma and OneSix

You don’t have to settle for outdated models and clunky workflows. With Sigma’s spreadsheet-native interface and direct access to cloud data, your team can move faster and smarter. As a Sigma consulting partner, OneSix can help you make a seamless transition to modern BI.

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