04-18, 15:30–16:05 (US/Eastern), Auditorium 5
Ever wished you could power live leaderboards for fitness challenges or dynamically award wellness badges in real time? Traditional OLTP systems often buckle under the pressure of continuous writes and aggregate reads. In this talk, we’ll explore how Moose, an open-source OLAP platform, enables rapid ingestion and lightning-fast queries on health and workout data. We’ll walk through a demo of creating real-time fitness leaderboards, awarding achievement badges, and using Python-based tools for data ingestion and visualization. Attendees will learn how an OLAP approach streamlines the architecture for modern wellness and health applications.
What & Why
Health and fitness applications produce constant streams of data, from workout logs and step counts to heart-rate measurements and sleep metrics. Crafting a dynamic, user-facing experience—like up-to-the-minute leaderboards or automated badge award systems—requires real-time data access and frequent aggregations. Traditional OLTP databases can stall under heavy reads and writes, making it tough to maintain a snappy user experience.
Enter Moose, an open-source analytics engine built around a columnar architecture. With Moose, developers and data teams can:
Ingest large volumes of real-time data from wearables, apps, and sensors.
Run near-instantaneous aggregations to power live dashboards or personal health insights.
Scale analytics cost-effectively thanks to Moose’s open-source foundation and Python-friendly ecosystem.
Practical Use Case: Real-Time Fitness Leaderboards
We’ll demonstrate how to build a workout leaderboard that updates in real time as users complete activities. We’ll also show how to apply custom rules for awarding achievement badges, ensuring that your application can both process and surface analytics-driven insights at scale.
Who Should Attend
Data & Analytics Engineers: Seeking solutions to handle large volumes of health/wellness data with frequent aggregations.
Developers/Architects: Building real-time or near-real-time consumer apps that rely on fast analytics.
Product Managers & Tech Leads: Interested in creating engaging features like live dashboards and automatic badge systems within their wellness offerings.
Health & Fitness Enthusiasts: Looking to understand how data architecture can enhance user engagement and personalized metrics.
A basic understanding of databases, Python data tools, and event streams (e.g., from wearable devices) is helpful but not required.
No previous knowledge expected
Chief AI Officer, computer scientist