Eon 发布 Eon Yukon：为 AI 智能体打造的无服务器数据引擎
Eon 刚发布了 Eon Yukon 数据引擎，专给 AI 智能体喂新鲜数据，基准里计算量比 Spark 省 4 倍、比 Flink 省 7 倍，基准还是公开的。
Eon 公司推出 Eon Yukon，一个为 AI 智能体设计的无服务器数据引擎，数据从接入、处理到服务在一条连续流中完成。事件在几秒内变成去重、排序的 Iceberg 表，通过 Redis 兼容 API 或 SQL 兼容查询引擎以亚毫秒延迟供智能体访问。在每秒 100 万事件、相同新鲜度的公开基准中，Yukon 的计算量比 Spark 少 4 倍，比 Flink 少 7 倍。Yukon 还通过 MCP 向智能体提供每张表的列级实时统计信息，并内置监控、采样和数据最小化功能。
Agent swarms will certainly break the data infrastructure we have built for humans. Agents always need the latest records and the full history to complete tasks correctly. Take a look at what @Eon_io_ is building: a new data infrastructure to keep data fresh as it arrives in open Iceberg tables in your own bucket. Agents get context on each table over MCP. In Eon's public benchmark, it used 4x less compute than Spark and 7x less than Flink. Ofir Ehrlich @OfirEhrlich What I'm seeing across the industry right now is that the biggest bottleneck to AI adoption isn't model capabilities. It's data infrastructure. Today, we're expanding the Eon Data Foundation with the launch of Eon Yukon, a new serverless data engine purposely built to change that. AI agents need fresh, contextualized data to act. Specialized models need a continuous feed of data and feedback to improve. Both are still served by a stack, an architecture, and methods built for dashboards: -Spark and Flink clusters sized for peak, plus compaction jobs to keep tables usable. -A pipeline and a copy for every consumer, staged bronze to silver to gold. -Hand-written jobs, wired together by an orchestrator and kept alive by specialists. The result: shipping data is complicated, risky, and expensive. While coding agents ship apps in hours, they rarely build or run data pipelines. The data is too volatile, and a mistake costs too much. With Yukon, data moves from ingestion to processing to serving in one continuous flow, without the infrastructure and pipelines traditionally required to connect them. Events become compacted, sorted, deduplicated Iceberg tables within seconds. Real-time aggregations run inline, with fully managed state. Agents and applications can access fresh data with sub-millisecond latency through a Redis-compatible API or through our SQL and spark compatible query engine. Beyond moving and serving data, Yukon makes it continuously understandable - Live column-level statistics, accessible through MCP, give agents visibility into the data. Built-in monitoring, sampling, and data minimization help teams maintain control. At 1M events/sec and equal freshness, Yukon used 4x less compute than Spark and 7x less than Flink. The benchmark is public: run it yourself with Eon’s ingest-bench. This is another major step toward our vision of an Autonomous Data Foundation for the AI era, built to both protect enterprise data and unlock its full potential. Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 5 🔄 0 ❤️ 10 👀 2211 📊 4 ⚡
