The best composable cdp

Summary

• Databricks defines a composable CDP as "a pattern for customer data platforms" that lets an organization choose a product for each layer: data collection, data storage and modeling, and data activation. It is built to use the existing source of truth in your lakehouse "rather than being locked into a single vendor product."
• No single product is best for every stack. The right choice depends on where your customer data lives, which layers you want to choose yourself, and how much integration and governance work your team can take on.
• Databricks says composable CDPs "reduce data movement, but they add another platform for data and marketing teams to manage, secure, govern and integrate." That is the trade-off to weigh against choosing each layer yourself.
• Databricks' own option is CustomerLake, an embedded CDP in Private Preview (announced June 16, 2026). Databricks also calls it "open and composable," designed to work with the marketing, advertising, analytics and customer experience tools teams already use.
• Compare candidates on layer coverage, source of truth, governance, activation, identity resolution, lifecycle stage and commercial terms, using the table below.

The best composable cdp

There is no single best composable CDP. The right one depends on where your customer data lives and which layers you want to choose separately. Pick a composable CDP when you want a separate product for each layer and want the lakehouse to stay the source of truth. If you would rather run fewer platforms, look at an embedded CDP such as Databricks' CustomerLake. It is in Private Preview, so confirm availability with your Databricks account team first.

What is a composable CDP?

Databricks' CDP explainer defines a composable CDP as a pattern that lets organizations choose a product at each layer of the CDP: data collection, data storage and modeling, and data activation. It "consists of the same components as their off-the-shelf counterparts." The difference is how they are assembled. Databricks says a composable CDP is built on the existing single source of truth in an organization's data lakehouse, "rather than being locked into a single vendor product."

What should you evaluate in a composable CDP?

Criterion	What to check
Layer coverage	Which of the three layers (collection, storage and modeling, activation) each product covers, and how the layers connect.
Source of truth	Whether customer data stays in your lakehouse or is copied into a vendor's store.
Governance	Whose access controls, lineage and audit records apply at each layer. Databricks says a composable setup adds another platform to manage, secure, govern and integrate.
Activation	Which destinations are supported, how audiences reach them, and what leaves your environment.
Identity resolution	How profiles are unified across sources and how edge cases are reviewed.
Lifecycle and terms	Whether each layer is generally available, and what the commercial terms are in writing.

How does Databricks approach customer data platforms?

Databricks describes the composable pattern in its CDP explainer, and it also offers CustomerLake, which it calls an "Agentic CDP embedded natively within Databricks." Its FAQ presents CustomerLake as the embedded alternative that brings "CDP capabilities natively into Databricks," and says it runs where your customer data already lives.
What Databricks says CustomerLake includes:

• Profile Agents turn raw customer data into business-ready Customer 360 profiles directly in Databricks.
• Campaign Agents use governed customer context to build audiences, recommend next-best actions, activate across channels, and continuously optimize experiences around business goals.
• Governance runs through Databricks and Unity Catalog, with centralized control over access, lineage and auditability across customer data, audiences and AI assets.
• Activation syncs audiences and signals from Databricks to martech, adtech and customer experience tools.
• Lakehouse Federation lets teams reach customer data in other systems where it resides.

FAQs

Is CustomerLake a composable CDP?

Databricks calls it an embedded CDP and contrasts it with composable CDPs in its FAQ. It also calls CustomerLake "open and composable" and says it is designed to work with the marketing, advertising, analytics and customer experience tools teams already use. Databricks positions it as the embedded alternative to a composable build.

Is CustomerLake ready for production?

Databricks lists CustomerLake as Private Preview: "CustomerLake is now available in Private Preview," announced June 16, 2026. It tells interested teams to contact their Databricks account team, and the launch post gives no general availability date. Confirm the current stage and support terms before you plan a rollout.

Can I use CustomerLake with data outside Databricks?

Yes, according to Databricks. Through Lakehouse Federation, teams can access customer data where it resides, including other warehouses, cloud object storage and operational databases. The Databricks docs describe that access as governed and read-only.

How does identity resolution work in CustomerLake?

Databricks says Profile Agents use Agentic Identity Resolution, which "combines deterministic, probabilistic and agentic matching to create golden Customer 360 records from fragmented identity data." It includes "LLM and human review workflows for edge cases." Teams can bring existing identity rules, models and third-party enrichment partners.