From the team
PhoenixAI Blog
Engineering deep-dives, product updates, and practical guides on real-time analytics and AI data infrastructure.
When Agents Write the SQL, Precomputation Stops Working
Materialized views work because dashboards ask the same questions every day. Agents do not. Here is what an analytical engine has to do when the query shape is decided at runtime.
Read the articleIntroducing PhoenixAI Anywhere — Self-Managed Real-time and AI Agent Analytics for private environments
The PhoenixAI database and the Anywhere Console that manages it.
How we made high-frequency upserts queryable in under a second on columnar storage
Analytical databases are columnar engines built for append-mostly data and large scans.
When Real-Time Meets Agents: Why We've Been on This Path All Along
Real-time workloads are moving from the specialized systems of a few teams to a shared expectation across the entire industry.
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.
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.
Analytical 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.
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.