Summary of DAIS 2025 Announcements Through the Lens of Games

Introduction

I hope you all were able to make it to the Data and AI Summit this year. For those of you who missed out, the available recordings should be posted in a few weeks. In the meantime, I want to share a few announcements from the show that I am most excited about and how they apply to games.

Lakebase

Our customers tell us: insight without action is wasted effort. Lakebase is an important step in the evolution of the Lakehouse as it seeks to power more action for game developers. Prior to Lakebase, all Lakehouse workloads were OLAP in nature. The latency and cost proposition didn't work well for transactional systems and lookups. This led to more static information, calculations, and KPIs being used for transactional systems, or duplicate efforts.

Lakebase enables game developers to easily serve Lakehouse derived insight to their applications. Lakebase is a fully managed Postgres database, integrated into your Lakehouse, that will automatically sync your Delta tables without you having to write custom ETL, config IAM or Networking. Learn more here (updated once available).

Example Use Cases

• Realtime Personalization: Serve up segments, offers and recommendations determined as a result of Lakehouse pipelines and/or machine learning models.
• Churn Mitigation: Maintain lists of users who have been determined to be at risk to churn as a result of ML models, statistical propensity and signals across your entire Lakehouse.
• External User Statistics: Power external websites that provide aggregated information about your player's experience with the game. Total kills across the game, current CCU, complex analytical leaderboards.

AI/BI Genie GA + Deep Research

AI/BI Genie, the foundation of our conversational analytics capabilities, is now generally available. This is one of the most exciting new capabilities for the Lakehouse. This tool enables you to have a conversation with your data and dive deeper into the insight you're being shown. Think ChatGPT but instead of using publicly available data, it can answer questions about your business data. With AI/BI Genie we've made a huge step towards making self service analytics a reality.

At DAIS we also announced the next evolution of the solution: Deep Research. AI/BI Genie Spaces are great at helping you answer "what happened?" Deep Research helps you answer "why did it happen?" Imagine seeing your CCU count drop in the last 3 days as compared to the last three months. What caused the drop? What factors contributed to that change? What trends can we spot? That's what deep research provides you. Learn More Here.

Example Use Cases

• Revenue/Marketing: Digging into the why behind a KPI or outcome
• Game Development: How are players engaging with the title
• LiveOps: Quickly understand what's happening and analyzing changing situations

Databricks Apps

Databricks Apps enables business users to engage with insight and quickly take action. So much of analytics is focused on telling the news, informing us of what has happened. Where Lakebase enabled action by empowering external applications to take advantage of Lakehouse insight and AI/BI Genie helped end users to engage with and understand more deeply the insight coming out of the Lakehouse; Databricks Apps empowers the creation of data focused applications.

Databricks Apps bring value to game developers of all sizes. Small teams will benefit from them as it will enable quick creation and deployment of interactive data applications and RAG style chatbots. Large organizations will be able to create flexible, sharable, ML interfaces empowering business users to benefit from more ML models. All organizations will rest easier knowing that the data is governed by the same permissions that are keeping their data secure as a whole. Learn More Here.

Example Use Cases

• A/B Testing: You're a LiveOps manager; you're working on a personalization project. Conversational analytics helped you figure out the different groups you want to receive specific offers. Now, using your Databricks App you can analyze and write back the new segment values into your Lakehouse (and serve them via Lakebase to your platform services).
• RAG Knowledge Bots: Create simple, easy-to-use, interfaces for RAG Chatbot applications like: One that brings in all your confluence docs for the development team or that exposes current lore to narrative and game designers.
• Persona Focused Data Applications: Enable your data developers, who best understand your Lakehouse, to quickly build applications for business users. For example, a Player Feedback Analysis tool that ingests data from Steam, X, Reddit, etc. extracts insight from the unstructured data, scores sentiment and enables the community manager to escalate reported issues.