Customer Data Platforms are entering a new stage. Many organisations expect more than identity stitching or campaign lists. They expect real-time intelligence, direct control of their data, and infrastructure that aligns naturally with their existing cloud environments. This expectation is creating a clear divide between traditional packaged CDPs and warehouse-native Composable CDPs.
Early CDPs centralized customer data by extracting it from the warehouse into a vendor-managed schema. Composable CDPs run directly inside the customer’s warehouse, executing capture, identity, analytics, AI, and activation without moving data.
Zeotap provides a packaged cloud CDP that centralises identity attributes, consent preferences, and customer profiles inside its managed environment, exporting that out to other tools via API connectors.
Architecture: Two Paths with Distinct Implications
Packaged cloud CDPs typically centralise customer data and profile storage within a vendor-managed application layer. Zeotap follows this pattern by maintaining unified profiles inside its CDP environment
Warehouse-native CDPs take a different architectural approach. Every Syntasa workflow from ingestion, identity, AI and activation, runs inside the customer’s cloud, using existing data warehouses and object storage.
Traditional CDPs rely on ongoing data replication, creating structural vendor lock-in over time. Warehouse-native CDPs use zero-copy access, keeping data in place and avoiding proprietary storage dependencies.
This reduces the need for additional external data stores and allows governance and security controls to remain unified under the customer’s policies.
Identity, Intelligence, and Activation Working in Sequence
Zeotap offers identity and consent-based profile management. Its consent mappings, consent rules, and profile attributes operate as part of a unified profile store.
In a warehouse-native model, identity resolution and AI models execute directly against warehouse tables. This keeps identity graphs, analytics, and activation continuously aligned with live data.
Syntasa extends identity workflows with continuous intelligence through four modules that operate as a single system:
- Data Ready prepares and structures data for modelling and identity.
- Core builds a unified identity graph using deterministic and probabilistic methods.
- Audience enables real-time audience building and activation.
- AI runs predictive and generative models inside the customer’s cloud.
Identity, analytics, and activation remain connected inside the customer environment, avoiding external data transfers.
Governance and Compliance
Zeotap offers identity and consent-based profile management. Its consent mappings, consent rules, and profile attributes operate as part of a unified profile store.
In a warehouse-native model, identity resolution and AI models execute directly against warehouse tables. This keeps identity graphs, analytics, and activation continuously aligned with live data.
Syntasa extends identity workflows with continuous intelligence through four modules that operate as a single system:
- Data Ready prepares and structures data for modelling and identity.
- Core builds a unified identity graph using deterministic and probabilistic methods.
- Audience enables real-time audience building and activation.
- AI runs predictive and generative models inside the customer’s cloud.
Identity, analytics, and activation remain connected inside the customer environment, avoiding external data transfers.
Real-Time Decisioning at Scale
Modern customer engagement relies on timely decisioning. Warehouse-native design supports streaming pipelines and millisecond-level orchestration. Syntasa can personalise on-site journeys, trigger session-based responses, and adapt content or offers during active browsing.
Packaged CDPs must first ingest events into their own storage, introducing latency and engineering overhead. Zeotap also supports real-time use cases, but the process is less intuitive, taking place through server-to-server ingestion and orchestration capabilities that must be manually built.
The key distinction lies in where the real-time decisioning executes: Because Syntasa executes real-time decisioning directly inside the customer’s warehouse, it enables true in-session personalisation with millisecond latency; Zeotap must first ingest data into its CDP, introducing engineering overhead and response delay.
Real-Time Decisioning at Scale
Modern customer engagement relies on timely decisioning. Warehouse-native design supports streaming pipelines and millisecond-level orchestration. Syntasa can personalise on-site journeys, trigger session-based responses, and adapt content or offers during active browsing.
Packaged CDPs must first ingest events into their own storage, introducing latency and engineering overhead. Zeotap also supports real-time use cases, but the process is less intuitive, taking place through server-to-server ingestion and orchestration capabilities that must be manually built.
The key distinction lies in where the real-time decisioning executes: Because Syntasa executes real-time decisioning directly inside the customer’s warehouse, it enables true in-session personalisation with millisecond latency; Zeotap must first ingest data into its CDP, introducing engineering overhead and response delay.
Implementation Without Added Complexity
Syntasa avoids migrations and additional data layers. Deployment occurs inside the customer’s cloud and integrates with existing pipelines, identity systems, and models. No duplicate storage is created. No new data capture layer is added. Teams can run Syntasa alongside an existing CDP before making platform-level decisions.
While Zeotap provides an accessible interface for marketing teams, it also introduces an external scaling dependency. The difference becomes visible when organisations manage multiple regions, large volumes of behavioural data, or specialised compliance conditions.
Where organisations require all data processing to stay inside their own cloud, warehouse-native deployment provides a streamlined path to integration with existing engineering and governance frameworks.
Packaged CDPs must first ingest events into their own storage, introducing latency and engineering overhead. Zeotap also supports real-time use cases, but the process is less intuitive, taking place through server-to-server ingestion and orchestration capabilities that must be manually built.
The key distinction lies in where the real-time decisioning executes: Because Syntasa executes real-time decisioning directly inside the customer’s warehouse, it enables true in-session personalisation with millisecond latency; Zeotap must first ingest data into its CDP, introducing engineering overhead and response delay.
Examples from Enterprise Practice
Real-Time Personalisation in Google Cloud
Syntasa avoids migrations and additional data layers. Deployment occurs inside the customer’s cloud and integrates with existing pipelines, identity systems, and models. No duplicate storage is created. No new data capture layer is added. Teams can run Syntasa alongside an existing CDP before making platform-level decisions.
While Zeotap provides an accessible interface for marketing teams, it also introduces an external scaling dependency. The difference becomes visible when organisations manage multiple regions, large volumes of behavioural data, or specialised compliance conditions.
Where organisations require all data processing to stay inside their own cloud, warehouse-native deployment provides a streamlined path to integration with existing engineering and governance frameworks.
Packaged CDPs must first ingest events into their own storage, introducing latency and engineering overhead. Zeotap also supports real-time use cases, but the process is less intuitive, taking place through server-to-server ingestion and orchestration capabilities that must be manually built.
The key distinction lies in where the real-time decisioning executes: Because Syntasa executes real-time decisioning directly inside the customer’s warehouse, it enables true in-session personalisation with millisecond latency; Zeotap must first ingest data into its CDP, introducing engineering overhead and response delay.
Automating Data Preparation and Governance
Another large enterprise used Syntasa’s Data Ready module to prepare data for Adobe Experience Platform. Schema creation, once a multi-day process, consistently completed in under 30 minutes. The improvement increased accuracy while reducing manual work for analysts and engineers.
Comparison Overview
Syntasa operates as a zero-copy, warehouse-native CDP with no required data migration or pricing tiers, while Zeotap relies on vendor-managed storage and tiered SaaS pricing.
Conclusion
As enterprises demand real-time intelligence and stricter governance, CDP architecture has become a strategic decision. Warehouse-native design aligns customer intelligence with long-term cloud and data strategy, restoring complete control.
SaaS CDPs and warehouse-native CDPs address different priorities. Many organisations now prioritise privacy, direct control, scalability, and continuous intelligence. Syntasa’s warehouse-native architecture supports these needs by keeping all activity inside the enterprise cloud and providing a unified foundation for identity, modelling, and real-time activation.
This approach reduces complexity, strengthens governance, and provides a clear path for enterprise-scale customer intelligence.
FAQs: Warehouse-Native vs. SaaS CDP
1. How does a warehouse-native CDP manage governance?
Governance inherits the organisation’s cloud policies. No additional frameworks are required because the data remains inside the customer’s environment.
2. Does a warehouse-native CDP require a data migration?
No. Data stays where it already resides. Pipelines, models, and activation operate in the existing cloud.
3. Can marketing teams use a warehouse-native CDP without technical expertise?
Yes. Syntasa provides a no-code and low-code interface for segmentation, activation, and analysis.
4. How does real-time activation function within the warehouse?
Event streams flow directly into the customer’s cloud. Syntasa processes events immediately and triggers audiences, offers, or recommendations within milliseconds.
5. How long does implementation take?
Most organisations reach production in four to eight weeks because no data movement or external replication is required.
6. Can Syntasa integrate with existing ML models?
Yes. Models from Vertex AI, SageMaker, Databricks, or custom containers integrate without rebuilding.