In the competitive gaming industry, understanding player behavior is key to delivering engaging experiences. Supercell, creators of Clash of Clans and Brawl Stars, faced challenges with fragmented data and limited visibility into user journeys. To address this, they partnered with Snowplow and Databricks to build a scalable, privacy-compliant data platform for real-time insights. By leveraging Snowplow’s behavioral data collection and Databricks’ Lakehouse architecture, Supercell achieved: Cross-platform data unification: A unified view of player actions across web, mobile and in-game Real-time analytics: Streaming event data into Delta Lake for dynamic game balancing and engagement Scalable infrastructure: Supporting terabytes of data during launches and live events AI & ML use cases: Churn prediction and personalized in-game recommendations This session explores Supercell’s data journey and AI-driven player engagement strategies.

Behind every powerful AI system lies a critical foundation: fresh, high-quality web data. This session explores the symbiotic relationship between web scraping and artificial intelligence that's transforming how technical teams build data-intensive applications. We'll showcase how this partnership enables crucial use cases: analyzing trends, forecasting behaviors, and enhancing AI models with real-time information. Technical challenges that once made web scraping prohibitively complex are now being solved through the very AI systems they help create. You'll learn how machine learning revolutionizes web data collection, making previously impossible scraping projects both feasible and maintainable, while dramatically reducing engineering overhead and improving data quality. Join us to explore this quiet but critical partnership that's powering the next generation of AI applications.

Securing Databricks using Databricks as SIEM showcases our approach on how we leverage Databricks product capabilities to prevent and mitigate security risks for Databricks. It demonstrates how Databricks can serve as a powerful Security Information and Event Management (SIEM) platform, offering advanced capabilities for data collection and threat detection. This session explores data collection from diverse data sources and real-time threat detection.

Join this session to hear how Western Governors University leverages Tealium & Databricks to power their data activation strategy with real-time customer data collection, activation and advanced analytics.

Each LLM has unique strengths and weaknesses, and there is no one-size-fits-all solution. Companies strive to balance cost reduction with maximizing the value of their use cases by considering various factors such as latency, multi-modality, API costs, user need, and prompt complexity. Model routing helps in optimizing performance and cost along with enhanced scalability and user satisfaction. Overview of cost-effective models training using AI gateway logs, user feedback, prompt, and model features to design an intelligent model-routing AI agent. Covers different strategies for model routing, deployment in Mosaic AI, re-training, and evaluation through A/B testing and end-to-end Databricks workflows. Additionally, it will delve into the details of training data collection, feature engineering, prompt formatting, custom loss functions, architectural modifications, addressing cold-start problems, query embedding generation and clustering through VectorDB, and RL policy-based exploration.