2026 Is When Open Data, Real-Time Analytics and AI Agents Converge
2026 is when open data, real-time analytics, and AI agents converge — driven by production-ready agents, “boring” Iceberg ops, and product-embedded analytics that users actually feel.
Read the articleAnalytical Agents — New Challenges for the Underlying Data Infrastructure
AI agents need more than BI. Open formats, sub-second latency, MCP, and a feedback-driven execution engine — what agent-native analytics actually demands.
Talk to your PhoenixAI Clusters Right From Claude: Introducing the PhoenixAI MCP Connector
Query your data, manage your clusters, and get cost insights conversationally — without ever leaving Claude Console. The PhoenixAI MCP connector for Claude is now available.
Meet Agent Fawkes — Your AI Copilot Inside PhoenixAI Cloud
Talk to your PhoenixAI data in plain English. Generate, fix, and optimize SQL without leaving the editor. Keep every byte of customer data inside your VPC.
Smarter Scaling in PhoenixAI Cloud BYOC
Spikes in concurrency, mixed workloads, and bursty traffic break traditional scaling. Smarter Scaling in PhoenixAI Cloud BYOC adjusts compute to match real-time demand.
Data Skew in Customer-Facing Analytics: The Hidden Cost Behind Latency
What data skew really is, why it’s especially dangerous in multi-tenant customer-facing applications, and how to solve it with a practical, production-ready approach.
5 Brilliant Lakehouse Architectures from Tencent, WeChat, and More
Slow lakehouse queries forced enterprises to copy data into proprietary warehouses. Modern query engines change that. Here are five lakehouse architectures from the field.