Guides and updates on QueryFlux, the open-source SQL query engine for federated data access across lakehouses.
5 articles
The future of data platforms isn't one engine. It's specialized engines on shared data, and a control plane that decides where each workload runs and how the tables stay fast.
AI agents are becoming primary consumers of Iceberg lakehouse data — querying tables iteratively, at high frequency, and without human review. This guide walks through the five components your infrastructure needs to support agentic workloads — MCP connectivity, guardrails, multi-engine routing, self-optimizing storage, and observability — and shows how LakeOps provides each one.
How to route queries across Trino, Spark, DuckDB, Snowflake, Athena, and Flink on shared Iceberg tables — covering the architecture of a SQL routing proxy, dialect translation, routing strategies, table-aware optimization, and the tooling that makes it work.
AI agents issue SQL iteratively, repeat query templates at high frequency, and need sub-second responses from tables designed for batch workloads. This post covers what breaks when agents hit a production Iceberg lake — and the five infrastructure layers that fix it: MCP connectivity, guardrails, multi-engine routing, self-optimizing storage, and closed-loop feedback.
QueryFlux is a universal SQL proxy and multi-engine query router in Rust—one access layer in front of Trino, DuckDB, StarRocks, and Athena with routing, dialect translation, and observability.
