Oracle在AI agents成为数据消费者时重新思考湖屋问题。
共同的语义层正在迅速成为为报告而建的湖屋和为人工智能而建的湖屋之间的分界线。
由于AI agents与人类分析师一起查询公司数据，企业需要能够理解分散在云层，格式和竞争系统中的信息的平台。 Oracle公司在过去一年里围绕这一转变重建了数据堆栈，这是更广泛的数据库现代化推动的一部分。 根据Oracle软件工程高级副总裁Nipun Agarwal(图片)的说法
“Lakehouses是客户用来进行分析的平台”,Agarwal 称。 “现在，湖屋已成为大赦国际的决策支柱。”
Agarwal在CUBE, SiliconANGLE Media的直播室独家播出时，他们讨论了Oracle的自主AI Lakehouse如何将共享的语义层与快速，实时对跨平台的数据访问相结合以支持AI agents. (* 下文披露。 )
在目录中构建一个开放的语义层
Oracle的方法基于一个可以挂载其他目录的目录，例如Databricks Inc.的Unity Catalog，以及Postgres和MySQL数据库，因此数据被询问到位而不是复制。 宣布公司是一个商业语义层，在十月事件上得到了更广泛的Apache Iceberg支持，根据Agarwal的说法，系统生成的表格和列描述也适用于非Oracle源。 他补充说，重要的是，Oracle不认为祭品与其他湖屋竞争。
“我们确实相信，对于任何企业来说，他们将使用多个数据来源。 他们将拥有多个湖泊。
Oracle的湖缓存通过将最常用的Iceberg表移动到Exadata的闪存中来加速查询。 实时访问也变得至关重要，因为软件agents成为了数据的主要消费者：根据Agarwal的说法，在agents提出要求时，被复制的数据的预定刷新可能会不足。
消费者不一定是人类，实时访问需要更多。
SiliconANGLE的部分内容和CUBE对Oracle AI World的报导：
(*披露：CUBE是Oracle AI World 2026赛事的付费媒体合作伙伴。 无论是CUBE事件报道的发起人Oracle还是其他发起人，都没有对CUBE或SiliconANGLE上的内容进行编辑控制。 ).
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Oracle rethinks the lakehouse as AI agents become data consumers
A shared semantic layer is fast becoming the dividing line between lakehouses built for reporting and those built for artificial intelligence.
As AI agents join human analysts in querying corporate data, enterprises need platforms that make sense of information scattered across clouds, formats and rival systems. Oracle Corp. has spent the past year rebuilding its data stack around that shift, part of a broader database modernization push. The company now pitches its Autonomous AI Lakehouse as an open layer that sits across an enterprise’s existing data estate rather than replacing it, according to Nipun Agarwal (pictured), senior vice president of software engineering, Oracle Database, at Oracle.
“Lakehouses were the platform which customers use for running analytics,” Agarwal said. “Now, lakehouses have become the decision backbone for AI.”
Agarwal spoke with theCUBE’s Dave Vellante at Oracle AI World, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how Oracle’s Autonomous AI Lakehouse combines a shared semantic layer with fast, real-time access to data across platforms to support AI agents. (* Disclosure below.)
Building an open semantic layer across catalogs
Oracle’s approach rests on a catalog that can mount other catalogs, such as Databricks Inc.’s Unity Catalog, along with Postgres and MySQL databases, so data is queried in place rather than copied. The company announced a business semantic layer and broader Apache Iceberg support at the October event, with table and column descriptions the system generates applying to non-Oracle sources as well, according to Agarwal. Crucially, Oracle does not see the offering as competing with other lakehouses, he added.
“We really believe that for any enterprise, they’re gonna have a choice,” he said. “They’re gonna be using multiple data sources. They’ll have multiple lakehouses.”
Oracle’s Lake Cache speeds up queries by moving the most-used Iceberg tables into Exadata’s flash cache. Real-time access is also becoming essential as software agents become the main consumers of the data: Scheduled refreshes of copied data can fall short when agents request it on demand, according to Agarwal.
“With AI that’s no longer the case, because for AI, the consumer is not a human necessarily,” Agarwal said. “The need for real-time access is a lot more with AI.”
Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of Oracle AI World:
(* Disclosure: TheCUBE is a paid media partner for the Oracle AI World 2026 event. Neither Oracle, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
Photo: SiliconANGLE
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