The Data + AI Summit is an annual conference hosted by Databricks that brings together data engineers, data scientists, machine learning practitioners, AI professionals, and organizational leaders to discuss advances in data management, analytics, and artificial intelligence. The summit serves as a platform for organizations to share technical implementations, best practices, and strategic insights regarding large-scale data infrastructure and AI integration.
The Data + AI Summit represents a key industry gathering for the data and AI community, featuring presentations from both Databricks technical teams and customer organizations implementing data mesh architectures and advanced analytics solutions. The conference addresses contemporary challenges in data organization, sharing, and utilization across modern enterprises. Sessions typically cover distributed data platforms, data governance frameworks, machine learning applications, and cross-organizational data collaboration patterns 1)
The summit spans multiple days with varying formats across editions. The 2026 edition (June 14-18, San Francisco) features a distinctive five-day structure that separates hands-on training from the main conference. The inaugural two days (Sunday-Monday) are dedicated exclusively to training sessions and skill development, allowing participants to engage deeply with technical workshops before the main conference begins 2).
The event includes multiple content tracks designed to serve different attendee roles and experience levels. Keynote presentations feature industry leaders discussing major trends and product announcements. Breakout sessions provide focused technical content on specific topics, tools, and use cases. The hands-on training courses enable participants to develop practical skills in current data and AI technologies. Certification exams offer professional credentials for attendees completing designated courses, supporting career advancement in data and AI roles 3)
The summit emphasizes practical implementations of enterprise data architecture patterns and emerging technical domains reflecting the current state of enterprise AI development.
The summit emphasizes practical implementations of enterprise data architecture patterns, particularly data mesh approaches that enable organizations to manage data as products across multiple cloud environments. Technical presentations showcase concrete use cases involving technologies such as Delta Lake, which provides ACID transactions and data governance capabilities for large-scale analytics workloads.
Notable technical topics include Delta Sharing, a protocol that enables secure, cross-organization and cross-cloud data sharing without requiring customers to move data or manage complex credentials. This capability addresses a critical operational challenge in multi-cloud environments where organizations must collaborate while maintaining data governance and security boundaries. Organizations implementing Delta Sharing can establish controlled access to datasets without creating duplicate copies or exposing underlying infrastructure 4)
Recent summits emphasize emerging technical domains including AI agents, which represent autonomous systems that can perceive environments, make decisions, and take actions with minimal human intervention. These systems integrate language models with planning capabilities, memory management, and tool interfaces to accomplish complex tasks 5)
Vibe coding is introduced as a novel curriculum topic, representing emerging approaches to software development that emphasize intuitive interaction patterns and novel developer experiences with AI systems. This reflects evolving methodologies in how developers interact with language models and AI-assisted programming tools for productivity and code generation 6)
The summit features presentations from major organizations implementing sophisticated data architectures. Industry examples demonstrate how enterprises architect solutions for complex requirements including multi-cloud data distribution, real-time analytics, and organizational data governance. Automotive manufacturers and other large-scale organizations have presented technical implementations addressing challenges such as coordinating data across distributed teams, managing data quality at scale, and enabling analytics across cloud providers.