Warehouse Dependent CDPs: a Double-Edged Sword in a Composable Enterprise

By Twilio Segment

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Warehouse Dependent CDPs: a Double-Edged Sword in a Composable Enterprise

In the shift toward agile, modular technology stacks, composable architecture has become a guiding principle for modern enterprises. A composable approach emphasizes the ability to mix and match world class, fit for purpose tools without being locked into rigid, monolithic systems or walled gardens.

Some CDP vendors have conflated the term “composable” to refer to their warehouse dependent approach which strays away from the guiding principles of composable. 

More and more companies are considering a warehouse-first data architecture, where a cloud data warehouse acts as the single source of truth for customer data. While warehouse-first strategies can deliver consistency, ownership, and scalability, deploying a warehouse depdendent CDP can paradoxically introduce architectural rigidity that can run counter to the goals of a truly composable architecture. 

What Is Composable Architecture?

A composable architecture is a modular data and technology stack built from interchangeable, interoperable components. It enables businesses to:

  • Rapidly integrate new tools

  • Swap technologies without overhauling the system

  • Maintain flexibility as use cases evolve

  • Tailor data flows and logic to their unique needs

Composable systems are built on APIs, cloud-native services, and components that can evolve independently.

What is a Warehouse-First Approach? 

In a warehouse-first approach to Customer Data , all customer data is funneled into a cloud data warehouse (like Snowflake, BigQuery, or Redshift) before it is exposed to other systems. Other systems of record and activation tools don’t resolve identity or store customer profiles themselves; instead, profiles are centrally stored in the warehouse.  

What role can the data warehouse play in a Composable Stack?

✅ Data Consistency

All tools (BI, CDP, ML models) read from the a single data source   

 You Retain Ownership, Visibility,  and Full Control over your data

Data lives in your warehouse, making it easier to change vendors for analytics or activation tools and avoid vendor lock-in

Centralized Governance

Security, compliance, and data modeling can be more easily managed, ensuring uniform policies across the stack.

Choosing a Warehouse Dependent CDP might seem like a perfect match, but there are several hidden tradeoffs: 

While the warehouse dependent CDP seems to align well with a warehouse first approach and exhibits some composable principles, it can paradoxically inhibit flexibility in other areas.

Not Designed for today’s Real-Time Needs

Expectations for immediate, personalized interactions and proactive operational responses are non-negotiable. Modern customer experiences like dynamic website personalization, AI Agents  and real-time triggered messaging (email, SMS, RCS, voice) often require low-latency decisioning. Data warehouses aren’t designed for this.  Warehouse dependent CDPs attempt to overcome this via complex workarounds like piping event data directly to downstream destinations or employing costly compute intensive workloads in an attempt to drive down latencies 

Accepting the  real time “gap”

The batch and queue based nature of data warehouses adds latency and relying solely on daily batch activation from the warehouse leads to missed opportunities

Example: A customer is in the process of applying for a loan on their mobile phone and only partially completes the process before being distracted by other things.  It may be hours or even days before your company can send a follow-up reminder for her to complete the process, leading to a lost revenue opportunity.  

Requires all customer data reside in the warehouse

Warehouse dependent CDPs can only operate on data that already exists in the warehouse.  This requires companies to dedicate significant time and resources to not only build and maintain pipelines to each and every data source and destination but also significant data engineering within the warehouse to ensure data is clean, compliant, error free, and mapped properly. The alternative is to have profiles that are starved of important data and context

Example: A customer is interacting with a customer service agent over live chat about a product issue. While the agent can access that customer’s profile that has basic information such as name, address, product purchased, etc, that profile lacks information about prior in-person visits to the retail store because the in store tracking system is not linked to the central data warehouse. 

Slow to Support New Use Cases

In a composable system, teams expect to be able to activate new use cases and leverage newly deployed tools quickly. But warehouse dependent CDPs require significant amounts of data engineering support to refactor data and configure schema within the warehouse. This creates friction and frustration  between data teams and business users who may have to wait days or weeks for access to new data or segments. This stunts agility, introduces complexity, and slows deployment times

Example: A marketing team wants to launch a campaign based on a sudden surge in product interest—but the relevant event data won’t be modeled and available in the warehouse for days.

Over burdens your Data Engineering Teams

Warehouse-first CDPs often lack built-in data collection, identity resolution, automated data governance, or segmentation tools. These must be built out and maintained by your team—an ongoing engineering burden that only grows as your stack expands. This clashes with the composable ideal of giving domain-specific teams autonomy to move fast

The Composable Paradox With Warehouse Dependent CDPs

A composable enterprise prioritizes speed, modularity, and team autonomy. Ironically, a warehouse-dependent CDP may undermine these principles:

  • It centralizes control but constrains agility.

  • It offers visibility but at the cost of velocity.

  • It enables transparency but can reduce independence for non-technical users.

Why Segment’s Customer Centric CDP is better for Composable Enterprises

  • True composability - easily connect ALL your existing customer touchpoints and easily add or change out ones in the future without the need to build out custom connectors or constantly refactor your warehouse

  • Real-time event collection, identity resolution, segmentation, and activation - enabling a wide array of real time use cases

  • Operate on all your customer data without the need for all of your data to transit through the warehouse

  • Interoperates with your data warehouse regardless of whether you are already use a cloud data warehouse or if you continue to use a mixed data strategy

  • Out of the box, native ID resolution and data governance accelerates deployment, but remains flexible enough to meet the needs as your business need change over time 

  • Tools for data teams to enable activation of multiple use cases while simultaneously enabling end users with a no-code means to self service  

Conclusion

The warehouse dependent CDPs (aka “composable CDPs”) often tout control, scalability, and customizability that fit well in a modern data stack. But in the context of a composable enterprise, they limit agility, especially for teams that depend on speed, autonomy, and real-time activation.

To fully realize the promise of composability, enterprises should be thoughtful about where and how they apply the warehouse-first principle—reserving it for governance and modeling, while supplementing it with agile, real-time, and event-driven tools  like Segment.

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