智能体集群必然会击垮我们为人类构建的数据基础设施。 智能体始终需要最新记录和完整历史才能正确完成任务。 看看 @Eon_io_ 正在构建的东西：一种新的数据基础设施，让数据在到达你自己 bucket 中的开放 Iceberg 表时保持新鲜。智能体通过 MCP 获取每张表的上下文。在 Eon 的公开基准测试中，它使用的算力比 Spark 少 4 倍，比 Flink 少 7 倍。
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.
【引用 @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.
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.在 X 查看被引用的帖子
来源：elvis · x.com