Abstract
As enterprises seek to move AI from proof-of-concept to production, standalone vector databases face limits in synchronization, ACID compliance, and resilience. This session shows how PostgreSQL-compatible distributed databases address these issues while keeping a familiar developer experience. Topics include: Building production-ready GenAI applications with distributed SQL, why standalone vector databases create production friction, scaling RAG architectures with pgvector across regions, multi-agent patterns in modern AI, and delivering ultra-resilience for peak traffic, grey failures, and disasters. We'll also dive deep into some critical data-centric design considerations for AI: open standards and flexible foundations, unified data sources, elastic scale requirements, compliance and security for multi-tenant environments, and enterprise reliability.
Software engineers and architects will gain practical strategies for building AI applications that scale globally while delivering the reliability, consistency, and performance enterprise systems demand.
Speaker
Karthik Ranganathan
Co-CEO and Co-Founder @Yugabyte
Karthik Ranganathan is Co-CEO and Co-Founder at Yugabyte, the company behind YugabyteDB, the open-source, high-performance distributed SQL database for building global, cloud-native applications.. Karthik was one of the original database engineers at Meta(Facebook), responsible for building distributed databases such as Cassandra and HBase. He is an Apache HBase committer, and also an early contributor to Cassandra, before it was open-sourced by Meta.
Session Sponsored By
The modern transactional database for cloud native applications
Date
Wednesday Nov 19 / 11:45AM PST ( 50 minutes )
Location
Pacific LM
Video
Video is not available
