Real-Time Analytics for AI Agents: Architecture Patterns for Production
How do you give AI agents accurate, current context without batching delays? This webinar covers the data infrastructure patterns behind production AI stacks — when to use vector stores, when you need real-time OLAP, and how to wire them together.
PhoenixAI platform walkthrough: ingest, query, scale
A full walkthrough of the PhoenixAI console — from connecting a Kafka stream to running complex analytical queries at sub-second latency.
The Real-Time Analytics Benchmark Report 2026
P99 latency, concurrency, and cost benchmarks — PhoenixAI vs. ClickHouse, Druid, and Trino at production scale.
Migrating from Apache Druid: what to expect and how to prepare
A technical session covering schema mapping, Kafka pipeline redirection, and parallel validation — with a live migration walkthrough.
The Real-Time OLAP Deployment Guide
Architecture patterns, capacity planning, and operational best practices for running PhoenixAI in production. From single-region to multi-cloud.
Vectorized execution: why it makes PhoenixAI 10× faster on analytical queries
A deep-dive into SIMD instructions, column-oriented storage, and vectorized execution — with live query profiling.
Data Infrastructure for AI Agents: What Retrieval Latency Actually Requires
Vector stores vs. real-time OLAP — what each is good for, where they fail, and how to combine them for production AI workloads.