Databricks vs Snowflake for marketing

Summary

• Databricks CustomerLake is an agentic customer data platform (CDP) embedded in the Databricks Lakehouse. Databricks announced it in Private Preview on June 16, 2026.
• Snowflake offers an AI Data Cloud for Marketing that covers customer 360 and identity resolution, Data Clean Rooms, attribution and incrementality tests, and AI and agents on governed marketing data.
• Both describe a governed foundation. CustomerLake is governed through Databricks and Unity Catalog, and Snowflake lists role-based access controls, dynamic data masking, and row-level security policies.
• Databricks says CustomerLake can reach customer data where it resides, including data in Snowflake, through Lakehouse Federation.
• A fair comparison weighs where your customer data lives, which collaboration and activation features you need, and each feature's availability stage.

Databricks vs Snowflake for marketing

Databricks and Snowflake each describe a governed data foundation for marketing teams, with different starting points. Databricks CustomerLake is an agentic CDP embedded in the Databricks Lakehouse, so its capabilities run on customer data in Databricks under Unity Catalog governance. Snowflake's AI Data Cloud for Marketing connects data, teams, and tools on one governed foundation. Databricks announced CustomerLake in Private Preview, so confirm current availability with your Databricks account team before you plan around it.

Databricks vs Snowflake: how do they compare for marketing?

Capability	Databricks	Snowflake
Positioning	CustomerLake, "a new Agentic CDP natively embedded in the Databricks Lakehouse"	"The AI Data Cloud for Marketing connects your data, teams and tools on one governed foundation"
Customer 360	Unifies behavior, transactions, engagement, product usage, and operational data into a governed Customer 360 directly in Databricks	Consolidates customer, behavioral, and campaign data from across the organization onto one governed platform
Identity resolution	Agentic Identity Resolution combines deterministic, probabilistic, and agentic matching	Resolves identities natively, enriches profiles with first- and third-party data, and offers identity resolution applications from providers native to the platform
AI and agents	Marketers create and refine segments in natural language with Genie, and Campaign Agents help identify the right audience	"Put AI and agents to work on governed and accurate marketing data"; Cortex Agents is a managed platform for building and running AI agents
Measurement	Agents that automate campaign planning, data preparation, personalization, and measurement	Runs attribution, media mix, and incrementality tests where the data lives
Partner collaboration	Databricks Clean Rooms, which uses OpenSharing (formerly Delta Sharing) and serverless compute	Snowflake Data Clean Rooms and Secure Data Sharing, with listings on Snowflake Marketplace
Governance	Unity Catalog: centralized control over access, lineage, and auditability across customer data, audiences, and AI assets	Role-based access controls, dynamic data masking, and row-level security policies

How does Databricks approach marketing?

CustomerLake builds the CDP into the platform where customer data already lives. Databricks' FAQ answers the question "Does CustomerLake move or copy my customer data?" with "No. CustomerLake is embedded directly in Databricks, so CDP capabilities run where your customer data already lives." Through Lakehouse Federation, teams can access customer data where it resides, including in Snowflake, cloud object storage, and operational databases.
On the activation side, CustomerLake syncs audiences and signals directly from Databricks to the martech, adtech, and customer experience tools that marketing teams already use. Outside CustomerLake, Databricks Clean Rooms lets multiple parties work together on sensitive enterprise data, and OpenSharing is the basis for both Clean Rooms and Databricks Marketplace.

What does Snowflake offer for marketing?

Snowflake's marketing page lists customer 360 with identity resolution, Data Clean Rooms that let partners collaborate while keeping raw customer data private, and attribution and incrementality analysis. Snowflake's documentation labels Data Clean Rooms generally available. Secure Data Sharing lets a provider share selected objects with other Snowflake accounts, and Snowflake's docs state that no actual data is copied or transferred between accounts.

What should you evaluate?

• Where your customer data lives: Databricks says CustomerLake runs where customer data already lives in Databricks and can federate to other sources. Check how each platform reaches the systems you use today.
• Availability: Databricks announced CustomerLake in Private Preview. Check the current stage of each feature you plan to use on both platforms.
• Collaboration needs: Compare Databricks Clean Rooms and Snowflake Data Clean Rooms against the partners you work with.
• Governance model: Compare Unity Catalog controls with Snowflake's role-based access, masking, and row-level policies.

FAQs

Can CustomerLake use data that is in Snowflake?

Databricks says yes. Through Lakehouse Federation, teams can access customer data where it resides, including in Snowflake.

Does Snowflake offer AI agents for marketing?

Yes. Snowflake's marketing page says to "put AI and agents to work on governed and accurate marketing data," and its docs describe Cortex Agents as "a fully managed agentic platform for building and running AI agents within Snowflake's governed environment."

Is CustomerLake generally available?

Databricks announced "CustomerLake is now available in Private Preview" on June 16, 2026. Contact your Databricks account team for current availability and access.

How do the two handle partner collaboration on customer data?

Snowflake's marketing page says to collaborate with partners through Snowflake Data Clean Rooms and keep raw customer data to yourself. In Databricks Clean Rooms, which use OpenSharing, collaborators cannot see the data in other collaborators' tables, views, or volumes, but they can see column names and column types and run approved notebook code that operates over the data assets.