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Databricks has used its annual Data + AI Summit in San Francisco to announce a series of product launches and an acquisition spanning AI agents, real-time analytics, data architecture, security and marketing technology.
The announcements include Genie One, an agentic AI assistant for business teams; LTAP, a new data processing architecture; Lakehouse//RT, a real-time analytics engine; CustomerLake, a customer data platform aimed at marketers; and the planned acquisition of security operations platform Panther.
Taken together, the announcements represent a significant expansion of the Databricks platform beyond its data engineering and analytics roots into enterprise software categories including security operations and marketing infrastructure.
Genie One Targets Business Teams With AI Agent Approach
Databricks has launched Genie One, an AI-powered assistant designed to help business teams across marketing, finance, sales and other functions automate and orchestrate their work using real enterprise data.
Genie One is part of a broader suite of Genie products that also includes Genie Agents, which allow teams to save conversations as reusable agents, and Genie App Builder, a managed environment for building business applications connected to governed enterprise data.
At the core of the Genie product family is Genie Ontology, a context layer that automatically extracts and updates business knowledge from Databricks and connected workplace applications including files, tickets, chats and meetings. The system connects to more than 50 applications and data systems, with integrations launching for Google Drive, Jira, Slack, Confluence and SharePoint among others.
Ali Ghodsi, Co-Founder and Chief Executive Officer of Databricks, argued that previous enterprise AI assistants have fallen short because they lack sufficient business context.
“Most enterprise AI today is just guessing with false confidence. That is not good enough for business,” Ghodsi stated.
“If you’re a CFO and AI can’t tell you why margins changed, or you’re a sales leader and it can’t find your next upsell, that’s not an AI problem, that’s a context problem.”
The Genie suite also includes Genie Code, an AI agent for data engineering and analytics workflows, and Genie ZeroOps, a background agent that monitors and proposes fixes for data and AI assets such as pipelines, jobs and tables.
Genie has no seat-based pricing. Organisations receive up to $10 free per user each month, with costs based on actual AI usage beyond that threshold. Genie One, Genie Agents and Genie Code are generally available, while Genie App Builder and Genie ZeroOps will enter private preview following the summit.
Customer Deployments Across Retail And Energy
Several organisations are already using Genie products. Albertsons Companies is applying the technology to what it calls Merchandising Intelligence, helping merchants make decisions across product, pricing, promotions and placement.
Karthik Iyer, Group Vice President and Head of Merchandising, Transformation and AI at Albertsons, explained that Genie plays a role in making complex merchandising data accessible to merchants through natural language, while the broader Databricks platform supports what the company describes as an end-to-end intelligence layer.
Krish Lakshminarayanan, Vice President of AI, Data and Analytics, Enterprise Architecture, and Matt Giunipero, Vice President of Data and Analytics at Foot Locker, described how Genie Agents are providing executives and business teams with a centralised space for AI-driven insights across the retailer’s North American operations.
“As we scale Genie to the enterprise, it’s reshaping the way our business interacts with data and makes the decisions that matter most,” the Foot Locker executives noted.
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LTAP Architecture Aims To Eliminate ETL
Databricks also introduced Lake Transactional/Analytical Processing (LTAP), a new data processing architecture that unifies transactions, analytics, streaming and operational data on a single copy of storage in the data lake.
The company pitched LTAP as an alternative to existing approaches for bridging transactional and analytical systems. Traditional architectures have required organisations to build change data capture (CDC) pipelines to move data between operational databases and analytical systems, a process Databricks described as brittle and prone to failure.
LTAP is powered by Lakebase, Databricks’ serverless Postgres offering built on open object storage. Lakebase already serves thousands of customers including Block, Ensemble, Superhuman and Zillow, handling 12 million database launches per day across the platform.
Ghodsi framed the architecture as a response to the demands of AI agent-driven development.
“For decades, complicated data infrastructure was a tax that teams were forced to pay,” he argued.
“Then agents arrived. In a matter of months, organisations effectively doubled their workforce, just not with humans. Agents write code, make calls, and run loops at a pace human teams never could. The infrastructure that powered the last era of computing is now the bottleneck that no one can afford. LTAP removes it.”
New Lakebase capabilities announced alongside LTAP include cross-cloud, cross-region disaster recovery, git-style branching and data snapshots for safe experimentation against production data, and autonomous database operations that allow agents to monitor health, detect slowdowns, propose indexes and assist with recovery.
LTAP stores data directly in Unity Catalog using open formats including Delta and Iceberg, with transactional and analytical workloads scaling independently. The architecture is listed as coming soon as part of Lakebase.
Lakehouse//RT Delivers Millisecond Query Performance
Databricks launched Lakehouse//RT, a real-time analytics capability powered by a new compute engine called Reyden that delivers millisecond query latency at tens of thousands of concurrent users and agents.
The company positioned Lakehouse//RT as an alternative to maintaining separate real-time serving layers alongside the lakehouse. Databricks argued that existing approaches force enterprises to choose between accepting latency or fragmenting their data stack with additional infrastructure that introduces vendor lock-in, governance gaps and data that is never truly current.
On standard analytical benchmarks, Lakehouse//RT delivers sub-100 millisecond latency at 12,000 queries per second. Customers have reported up to 16 times better performance than their existing real-time serving stacks, with response times as low as 10 milliseconds on smaller datasets.
“Over the past decade, we’ve unified the major workloads of the modern data stack on a single open foundation,” Ghodsi explained.
“Lakehouse//RT completes the engine spectrum, providing the millisecond speed layer that people want and agents require.”
Chris Kopek, Head of Data Platforms at Cisco, described how the technology is being evaluated for threat lookup workloads.
“What we’re seeing with Lakehouse//RT is millisecond performance on live data with 5x improvement in response time, which creates a path to run those workloads on our lakehouse instead of maintaining a separate serving system,” Kopek outlined.
Kayvon Raphael, Senior Director of Engineering at Magnite, reported sub-200 millisecond performance on core dashboard queries, noting the reduction in complexity from keeping everything governed inside the company’s own data lake.
Lakehouse//RT is now available in beta.
Panther Acquisition Bolsters Security Ambitions
Databricks has agreed to acquire Panther, an AI security operations centre (SOC) platform, in a deal intended to advance the company’s push into the security software market.
The acquisition is the third security-related deal announced by Databricks, following its acquisitions of Antimatter and SiftD.ai. Panther’s customers include Anthropic, the AI safety company behind the Claude AI model.
Databricks launched Lakewatch, its security lakehouse product, earlier this year. The company argued that legacy security information and event management (SIEM) tools are held back by high costs, limited data and manual workflows, leaving most organisations analysing only a fraction of their security data.
Panther brings more than 100 pre-built data integrations, detection-as-code capabilities and agentic SOC workflows designed to automate threat investigation. The platform was founded by the creator of the open source StreamAlert project originally built at Airbnb.
“Legacy SIEM was never designed for AI,” Ghodsi contended.
“With Panther, we enhance and expand our ability to analyse all data and automate SOC workflows. Together, we can offer a platform to help defend the world against agentic attacks.”
Jack Naglieri, Founder and Chief Executive Officer of Panther, described the SOC as being at an inflection point.
“AI is changing how attacks are launched and defenders can now finally keep pace with them,” Naglieri stated.
“Together with Databricks, we can arm defenders with agents that scale detection, investigation and response.”
Tim Nguyen, Head of Defence at Anthropic, noted that Panther has helped the AI company bring a software engineering approach to detection and response, giving the security team flexibility to adapt as its environment evolves.
The acquisition is subject to customary closing conditions including regulatory clearances.
CustomerLake Takes Aim At Marketing Technology
Databricks also entered the marketing technology category with CustomerLake, an agentic customer data platform (CDP) that unifies customer data, AI models, agents, identity resolution, audience building and activation on the Databricks platform.
The company described CustomerLake as a replacement for traditional CDP architectures, which it characterised as following a waterfall model where campaigns are planned and executed across dozens of disconnected systems, take weeks to ship and leave customer data siloed outside the company’s core AI platform.
CustomerLake introduces what Databricks calls “infinity campaigns” – continuous agent-driven engagement loops that replace one-off campaigns and react to customer context in real time. The system includes campaign agents for audience building and personalisation, profile agents for turning raw customer data into business-ready records and an open partner ecosystem.
Launch partners and integrations span a range of marketing and advertising technology providers including Adobe, Meta, Acxiom, Epsilon, LiveRamp, The Trade Desk, Braze, Bloomreach, Iterable, Snapchat, Magnite, TransUnion, Adstra, Twilio, Integral Ad Science (IAS) and Unity.
“Marketers need to reimagine their entire foundation – not just the campaigns they run, but the customers they run them for, which now include agents,” Ghodsi argued.
“With CustomerLake, customer data, AI models and agents live in one governed platform. Marketing stops being a series of campaigns and becomes a continuous loop.”
Current CustomerLake customers include HP, Circle K, AB InBev and Getnet by Santander. Kumar Ram, Global Head of Marketing Technology and AI Enablement at HP, described the platform as enabling HP to build customer intelligence, personalisation and activation on the data foundation the company already trusts, rather than creating another location where data must be copied, reconciled and secured.
Jay Goebel, Vice President of Data Partnerships at The Trade Desk, noted the partnership is enabling marketers to connect trusted data and agentic AI with media execution, giving brands a faster path from customer insights to action.
Chris Feo, Senior Vice President of Sales and Partnerships at Unity, highlighted the scale of the company’s mobile ecosystem, which includes 256 million monthly active users in the US and 2.85 billion globally, and described the CustomerLake integration as making Unity inventory addressable from the data foundation brands already manage in Databricks.
CustomerLake is available in private preview. Pricing will be based on a consumption model rather than a traditional software licence.
Databricks Expands Platform Footprint
The breadth of announcements at the summit reflects Databricks’ strategy of expanding from a data engineering and analytics platform into adjacent enterprise software categories. The company now counts more than 20,000 organisations as customers, including 70 per cent of the Fortune 500, with clients spanning adidas, AT&T, Bayer, Block, Mastercard, Rivian and Unilever among others.
The company is headquartered in San Francisco and operates more than 30 offices globally. Its platform now encompasses Lakebase, Genie, Agent Bricks, Lakeflow, Lakehouse and Unity Catalog, with the security and marketing launches extending the platform into categories traditionally served by specialist vendors.
Last Updated on June 18, 2026 by Nick Ross
