Building a composable CDP on Snowflake
Joey Lee
December 29, 2025
For years, customer data platforms promised a single view of the customer. In practice, many of these platforms became monolithic systems that duplicated data, locked business logic inside proprietary tools, and struggled to adapt as marketing stacks evolved.
A new approach has emerged in response: the composable CDP. Instead of relying on a single vendor, teams assemble best-in-class tools around a central data platform. Increasingly, that platform is Snowflake.
By combining Snowflake with reverse ETL and modern engagement tools, marketers can build a flexible, scalable CDP that evolves with their business.
What is a composable CDP
A composable CDP is not one product. It is an architecture.
At its core, it consists of:
A cloud data warehouse as the system of record
Tools for ingestion and transformation
Tools for activation and orchestration
Business logic and customer definitions live in the warehouse rather than inside any one application. This makes the system modular and easier to adapt over time.
Snowflake is particularly well-suited to this role because of its scalability, governance features, and ability to support both analytics and activation workloads.
Snowflake as the system of record
In a composable CDP, Snowflake stores raw event data, customer attributes, and modeled customer tables. Data from websites, mobile apps, CRMs, billing systems, and ad platforms all flows into the warehouse.
Inside Snowflake, teams build a unified customer profile that can be used across marketing, product, and analytics use cases. Because Snowflake supports structured and semi-structured data, it can handle high-volume event streams alongside relational data.
Snowflake’s architecture allows multiple teams to work with customer data simultaneously without performance trade-offs.
Transforming data with ELT and dbt
Once data is in Snowflake, transformations happen using ELT workflows. Tools like dbt allow teams to define customer models, metrics, and segments directly in the warehouse.
This includes:
Identity resolution and customer stitching
Lifecycle stage definitions
Derived attributes like lifetime value or engagement scores
Because these transformations are version-controlled and transparent, they are easier to maintain and audit than logic embedded in traditional CDPs.
Activation through reverse ETL
Reverse ETL is what turns a warehouse-centric CDP into an operational system. Tools like Hightouch and Census sync data from Snowflake into ESPs, CRMs, ad platforms, and other marketing tools.
Instead of redefining segments in each destination, teams define them once in Snowflake. Reverse ETL keeps downstream systems updated automatically.
This enables:
Consistent segmentation across channels
Faster experimentation and iteration
Clear ownership of business logic
Orchestration with ESPs and CDPs
Engagement platforms like Braze, Iterable, Customer.io, Simon Data, and Bloomreach sit on top of this foundation. They handle journey orchestration, messaging, and channel execution.
In a composable CDP, these tools do not own the data. They consume it from Snowflake. This separation allows teams to switch tools or add new channels without rebuilding their data foundation.
Some platforms, like Simon Data, are designed to operate directly on top of Snowflake, further reducing data movement and duplication.
Why marketers are moving to composable CDPs
Composable CDPs solve many of the problems associated with traditional platforms:
Less data duplication and vendor lock-in
Greater transparency and control over business logic
Better alignment between analytics and activation
Easier integration with new tools
For agencies and large organizations, this flexibility is especially valuable. Each client or business unit can share a common data foundation while tailoring activation tools to their needs.
Challenges to plan for
Composable architectures require stronger data foundations. Identity resolution, data quality, and governance must be handled carefully. Teams also need clear ownership between marketing, data, and engineering.
Snowflake helps address these challenges with built-in security, access control, and scalability. But success still depends on good data modeling and collaboration.
Snowflake as the foundation for modern CDPs
The shift to composable CDPs reflects a broader trend in marketing technology. Data is no longer confined to individual platforms. It lives in the warehouse and flows outward.
Snowflake enables this shift by providing a reliable, scalable, and neutral foundation for customer data. Combined with reverse ETL and modern engagement tools, it allows marketers to build CDPs that are flexible, future-proof, and aligned with how businesses actually operate.
In the next post in this series, we will explore cost, performance, and operational best practices for running Snowflake at scale.
Inside Snowflake’s architecture: The magic behind the scenes
Building a composable CDP on Snowflake
