Introducing CustomerLake: The Agentic CDP embedded in Databricks

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Summary

Databricks is introducing CustomerLake, a new Agentic CDP natively embedded in the Databricks Lakehouse. CustomerLake brings Customer360, identity resolution, segmentation, activation, and personalization directly to the governed data and AI foundation enterprises already use, helping teams move faster without moving or duplicating sensitive data.

CustomerLake replaces manual, fragmented martech workflows with autonomous agentic capabilities. Profile Agents help marketers and data teams turn raw customer data into business-ready Customer 360 profiles, while Campaign Agents help build audiences, recommend next-best actions, activate across channels, and continuously optimize around business goals.

CustomerLake is open and interoperable across the same martech and adtech ecosystem enterprises already use. With native integrations across identity, activation, and engagement partners, Unity Catalog governance, and Lakehouse Federation, enterprises can activate trusted customer context across clouds, platforms, and channels without creating new silos or adding vendor complexity.

Today at Data + AI Summit, we’re announcing Databricks CustomerLake, a new Agentic Customer Data Platform (CDP) natively embedded in Databricks. CustomerLake brings core CDP capabilities, including Customer 360, identity resolution, audience building, campaign automation, activation, and personalization, directly into the lakehouse where customer data, AI models, and governance already reside. With CustomerLake, marketing and data teams work together on a shared, governed foundation to turn customer data into always-on, 1:1 customer experiences. Instead of relying on manual campaign work and disconnected systems, marketers can deploy agents that continuously analyze behavior, decide, and act, delivering intelligent engagement at enterprise scale without creating new silos, duplicating sensitive data, or adding martech complexity.

Extracting value from customer data remains one of the hardest challenges in marketing. Most enterprises still operate across fragmented identities, stale audiences, and long queues of data requests. Golden customer records can take months to build and unify, and every new martech tool creates another place where sensitive customer data must be copied, secured, and governed. At the same time, marketing is entering a new era. AI is raising the bar for customer engagement — consumers are beginning to use agents to browse, compare, and make decisions on their behalf in seconds. To keep up, marketers need to engage customers faster, across more channels, and with greater personalization. Rebuilding marketing for agents For decades, enterprises have invested heavily in customer data infrastructure: data warehouses, data lakes, CDPs, CRMs, marketing automation platforms, identity providers, advertising platforms, and analytics tools. Yet marketers still struggle to answer basic questions quickly: Which customers are most likely to churn? Which audience should receive this offer, and in which channel? Which campaign actually drove incremental impact? The problem is architecture, not strategy. Existing CDPs help unify customer profiles and activate audiences, but they sit outside of the company’s core Data and AI platform. That creates another system to integrate, govern, and reconcile. Agentic marketing requires a different foundation. To truly personalize at scale, agents need governed access to customer identity, predictive models, business logic, activation endpoints, and real-time performance signals. They need context, intelligence, and execution in the same place. CustomerLake provides that single environment by bringing the CDP into the Databricks lakehouse, unifying governed customer data, AI models, and agents to power always-on, truly 1:1 marketing. Marketers need to reimagine their entire foundation. Not just the campaigns they run, but also the customers they run them for, which now include agents. With CustomerLake, we're replacing legacy software with an open, Agentic CDP built directly on the Lakehouse. When customer data, AI models, and agents live in one governed platform, marketing stops being a series of campaigns and becomes a continuous loop – agents that constantly analyze, decide, and act on every customer in real time. For the first time, enterprises can deliver true 1:1 experiences at an infinite scale. — Ali Ghodsi, Co-Founder and CEO of Databricks Introducing CustomerLake: The Agentic CDP built natively in Databricks CustomerLake combines the core capabilities marketers expect from a CDP — including Customer 360, identity resolution, audience building, campaign automation, activation, and personalization — with the governance, scale, and security of the lakehouse. Because CustomerLake is embedded in Databricks and governed by Unity Catalog , it remains interoperable across the enterprise data estate. Through Lakehouse Federation , teams can access trusted customer data where it resides, whether in Databricks, Snowflake, Google BigQuery, cloud object storage, operational databases, or other enterprise systems. At the center of CustomerLake are two core agentic capabilities: Profile Agents: Turn raw customer data into business-ready Customer 360 profiles directly in Databricks. Profile Agents prepare data, identify quality issues, and support third-party data enrichment to unify disconnected records into trusted golden profiles. Campaign Agents: Help marketers move from static, one-off campaigns to always-on engagement. Campaign Agents use governed customer context to build audiences, recommend next-best actions, activate across channels, and continuously optimize experiences around business goals.

CustomerLake brings marketing into the AI era with a new operating model built on three core principles: Embedded: Build a unified, governed and AI-ready Customer 360 directly on your data foundation, eliminating martech complexity, data duplication and unnecessary movement. Democratized: Empower marketers with agent-first interfaces to build audiences, automate campaigns and activate experiences on trusted data, while reducing ad hoc requests and operational overhead. Autonomous: Power 1:1 personalization at scale with agents that continuously analyze customer signals, recommend next-best actions and optimize engagement around business goals....