The Composable CDP category is growing fast – but clearly not all architectures are created equal. A case in point is Reverse ETL (“extract, transform, load”), which many vendors use to move data out of their warehouse and into marketing platforms. Once upon a time, these tools solved a key challenge – getting data where it needed to go – but they are limited, and they stop short of activating intelligence.
Syntasa’s Composable CDP represents the next evolution. Rather than sending data elsewhere, it runs natively inside your cloud to unify, model, and activate customer intelligence in real time. The result is a warehouse-native Composable CDP that gives enterprises full control over their data, governance, and performance.
When Reverse ETL Hits Its Limits
Reverse ETL tools were designed to bridge the gap between a data warehouse and operational systems. They move tables or query results from storage into CRMs, advertising platforms and analytics environments.
But even when warehouse-native, this approach hits boundaries: identity often fragments, governance becomes more complex, and real-time decisioning can be constrained. Multiple synced copies across SaaS tools increase latency, duplicate data, and deepen compliance risk.
How Syntasa Takes It Further
Syntasa also uses a warehouse-native model (deployed inside AWS, GCP or Azure inside the customer’s environment) but it’s built as a full customer intelligence platform rather than simply a sync engine.
- It unifies ingestion (batch + real-time), identity resolution (persistent ID graph), and audience activation across all customer data.
- Because activation, intelligence and audiences live inside the same environment, there is no need to push data into multiple downstream SaaS copies and manage disparate governance models.
- The modular architecture (Data Ready → Core → Audience → AI) means teams can go from insight to activation without stitching standalone tools together.
- For enterprises already invested in cloud infrastructure, it’s a logical next step from “syncing data” to operationalizing data with identity, audiences and AI in one governed warehouse-native stack.
Composable CDP = Reverse ETL + Identity + AI + Governance
In large enterprise stacks, Reverse ETL is only one node in a complex orchestration chain. Teams still need a warehouse for storage, notebooks or data build tool (dbt) for modeling; an identity graph for user stitching; and Reverse ETL for activation. Each new connection adds cost, latency, and risk.
Syntasa’s Composable CDP collapses that entire chain. Running natively inside your existing GCP, AWS, or Azure environment, it unifies ingestion, identity resolution, AI modeling, and activation within one governed framework. For instance, instead of maintaining five vendors and a dozen syncs, teams operate a single composable layer where every function shares the same data, permissions, and context.
This architecture means faster pipelines, lower integration overhead, and complete control over compliance – all while keeping data inside the organization’s own cloud environment.
Real-World Pain Points that Reverse ETL Won’t Fix
Even the best Reverse ETL pipelines are not built to solve every enterprise need, even with strong sync capabilities.
1. No Built-In AI Modeling Environment
Most models are SQL or dbt-based; there is no full ML studio for training or deploying AI models inside your cloud. That means teams still need external tools to build predictions, recommendations, and next-best-action models.
2. Identity Resolution Is Still Model-Dependent
Most reverse everse ETL tools can ingest behavioral event data into the warehouse and use it in audiences, but its identity resolution is still driven by the identifiers and rules you define across your warehouse models rather than a continuously managed CDP identity layer. Syntasa is warehouse-native, so identity stitching and behavioral context are maintained as part of the same in-cloud system that feeds analytics, AI, and downstream activation.
3. Activation Still Requires Syncing to the Last-Touch Tool
Hightouch activates by syncing warehouse data to destinations, including real-time audiences that can sync membership changes in seconds, and the final execution still happens in the destination tool. Syntasa works the same way at the last mile, but keeps decisioning, identity, and modeling warehouse-native so updates are computed where the data lives before syncing to Adobe Target, Meta, or email platforms.
Action Data with Production-Ready AI and Attribution Inside Your Cloud
Reverse ETL stops the moment data lands in a destination. Syntasa’s Composable CDP starts where activation-only tools end by giving teams access to a full library of prebuilt AI models, a DIY AI Studio, intelligent AI agents, and native attribution models that all run inside the customer’s cloud environment.
This combination gives marketers and data scientists something other tools can’t: AI and attribution that train, deploy, and iterate directly on warehouse data, linking identity, behavior, outcomes, and media touchpoints with no copying, no black boxes, and no loss of granularity.
Pre-built models are tuned to your own data in one to two weeks, providing a fast path to production for use cases like propensity scoring, churn modeling, recommendations, and multi-touch attribution across paid, owned, and onsite channels.
Teams can also build full notebook-based models using Python, TensorFlow, PyTorch, and related frameworks directly on the unified dataset. Data scientists retain full control of their IP while attribution logic, features, and outcomes remain governed inside the customer’s private cloud.
Action Data with Production-Ready AI and Attribution Inside Your Cloud
Retail-grade recommendation engines: Merchandising and eCommerce teams can deploy Syntasa’s prebuilt or custom recommendation models that learn from browsing, purchase patterns, and predicted intent. A leading UK electronics retailer generated £23M in incremental revenue using Syntasa’s AI-driven recommendations.
Churn detection tuned to your business: AI models surface early behavioral signals of inactivity so marketers can trigger personalized retention journeys before a customer lapses – all using first-party data that stays inside your cloud.
Inventory-aware activation: Prebuilt demand, stock, and price-sensitivity models connect product availability with real-time marketing decisions, helping teams prioritize offers that drive both conversion and margin.
Reverse ETL helps move data. Syntasa gives teams the AI to do something meaningful with their data immediately and securely using prebuilt models or models they can design themselves.
This combination gives marketers and data scientists something other tools can’t: AI and attribution that train, deploy, and iterate directly on warehouse data, linking identity, behavior, outcomes, and media touchpoints with no copying, no black boxes, and no loss of granularity.
Pre-built models are tuned to your own data in one to two weeks, providing a fast path to production for use cases like propensity scoring, churn modeling, recommendations, and multi-touch attribution across paid, owned, and onsite channels.
Teams can also build full notebook-based models using Python, TensorFlow, PyTorch, and related frameworks directly on the unified dataset. Data scientists retain full control of their IP while attribution logic, features, and outcomes remain governed inside the customer’s private cloud.
Simplicity Without Sacrifice
Reverse ETL tools earned their popularity by being simple. But simplicity shouldn’t require losing control. Syntasa’s no-code and low-code interface gives marketers the same ease of use within a governed environment IT can trust.
Data teams keep full visibility into pipelines, lineage, and performance, while analysts and marketers enjoy intuitive workflows for audience creation, modeling, and activation. Because Syntasa runs on your own cloud, there’s no vendor lock-in and no barriers to scale. It’s as simple as Reverse ETL, but enterprise-grade from day one.
Proof in Production
Two recent Syntasa deployments show how this architecture performs in practice.
Case Study: AI-Driven Personalization and Real-Time Activation
A leading electronics retailer in the UK and Ireland implemented Syntasa’s Composable CDP within its Google Cloud Platform environment. As a direct result, the retailer unified customer data across channels, creating over 22 million Customer 360 profiles.
In addition, real-time activation helped to generate £45 million+ in revenue from cart-abandonment campaigns and £20 million from web personalization initiatives.
By replacing fragmented batch processes with unified, real-time intelligence, they improved marketing efficiency, personalization accuracy, and revenue performance – all while maintaining full compliance with evolving privacy standards.
Case Study: Automating and Scaling CDP Data Ingestion
A global electronics retailer deployed Syntasa’s Composable CDP inside its private cloud to unify and operationalize customer data at enterprise scale. Prior to Syntasa, ingesting and managing data across web behavior, transactions, product data, consent signals, and contact center systems created fragmentation and slowed activation across regions. By standardizing ingestion pipelines and automating data readiness inside its own cloud environment, the retailer established a governed, real-time foundation for identity resolution, AI modeling, and activation. This approach enabled the creation of more than 169 million unified customer profiles and supported global personalization, paid media activation, and in-session decisioning without copying data into external SaaS systems. The result was a scalable, compliant data foundation that accelerated CDP maturity while supporting over $200M in incremental revenue across North America, EMEA, and APAC.
The Next Step After Reverse ETL
Reverse ETL built the bridge between data warehouses and marketing tools. Syntasa takes the next step by unifying data, identity, AI, and activation directly inside the warehouse. It offers everything a Reverse ETL tool can, and then some. Syntasa’s Composable CDP delivers:
- Real-time activation and millisecond decisioning.
- End-to-end governance under your existing cloud policies.
- Integrated AI modeling and predictive analytics.
- Flexibility to scale across teams, use cases, and industries.
For enterprises already running on GCP, AWS, or Azure, adopting a warehouse-native Composable CDP reduces complexity while improving insight speed and compliance confidence.
Syntasa vs. Reverse ETL’s: Using Hightouch as a Use Case
Hightouch is a good example of a Reverse ETL tool that has slowly added on more capabilities to mimic a truly composable CDP. While it has its use-cases, these tools add layers of complexity and more limited functionality.
Capability | Syntasa | Hightouch |
Core Purpose | Unified Data + AI Platform for Composable CDP, modeling, and activation within your own cloud. | Data-activation layer that syncs warehouse data to external tools. |
Architecture | Warehouse-native; runs fully inside GCP, AWS, or Azure with zero data movement. | SaaS-based; moves data out of the warehouse to 250+ connected apps. |
Identity Resolution | Built-in ID Graph with deterministic and probabilistic matching for real-time stitching. | Identity resolution only available on certain product tiers. |
AI & Predictive Modeling | Integrated propensity, churn, and GenAI modules for real-time insights and actions. | No built-in AI; relies on external ML pipelines or dbt integrations. |
Real-Time Activation | Stream-based activation real-time decisioning across channels. | Only available on certain tiers, and relies on API calls subject to latency and sync schedule setting. |
Ease of Use | No-code / low-code UI for marketers plus pro-code flexibility for data teams. | Simple UI for syncs; limited modeling and analytics tools. |
Deployment Flexibility | SaaS, Virtual Private Cloud, On-Prem, or Air-Gapped for sensitive sectors. | Multi-tenant SaaS primarily; no private or on-prem option. Some hybrid deployments are documented but still rely on SaaS control plane. |
Integration Scope | Hundreds of inbound/outbound integrations across martech, adtech, and data ecosystems. | 250+ outbound connectors for CRMs, ads, and productivity tools. |
| Security & Data Control | Segmentation happens within the customer cloud. Data never leaves the customer environment; fully auditable and transparent. | Data is read from warehouse during segmentation processs; limited visibility post-sync. |
| Typical Users & Scale | Enterprise marketing, AI, and data teams managing billions of records securely. | Growth and RevOps teams syncing warehouse data to tools quickly. |
Conclusion
Reverse ETL was an important step in the data-activation journey, but it’s no longer enough for enterprises that demand speed, scale, and intelligence within their own environment. Syntasa’s Composable CDP unites first-party data control, integrated AI, and warehouse-native design to deliver personalization and decisioning at enterprise scale , without data leaving your cloud.
Explore how Syntasa helps organizations transform their warehouses into live engines of customer intelligence.
FAQs
1. What does a typical implementation timeline look like when moving from Databricks to Syntasa?
Enterprises can begin production use within 4–8 weeks, depending on data volume and complexity. Syntasa’s modular design means teams often start with Data Ready for ingestion and cleansing, then add Core for identity resolution and AI for predictive modeling. Most clients keep their Databricks environment active during onboarding, using parallel runs before switching to full Syntasa orchestration to ensure continuity.
2. Do we need to rebuild our existing models or pipelines to use Syntasa?
No. Syntasa can ingest, reference, and execute existing models built in Databricks, Vertex AI, or other ML frameworks. Teams can containerize current models or migrate gradually using Syntasa’s model orchestration layer. This flexibility reduces rework and allows gradual consolidation of decisioning logic into one governed environment.
3. What kind of technical and operational support does Syntasa provide during migration?
Syntasa offers a dedicated implementation team and solution architects who map current data flows, connectors, and identity logic from your Databricks environment. Migration includes automated schema mapping, governance alignment, and performance benchmarking within your cloud. For regulated sectors, Syntasa provides VPC- and air-gapped deployment support plus audit-ready documentation aligned with GDPR, HIPAA, and SOC 2 standards.
4. Are there risks in switching from Databricks or Reverse ETL to Syntasa?
Risk is minimal. Syntasa runs inside your existing cloud (GCP, AWS, or Azure), so no data or workloads need to move. Migration happens in parallel with your current stack, letting teams validate before go-live.
Safeguards include:
- Zero data movement: Everything stays within your governed environment.
- Automated mapping: Existing schemas and pipelines sync through pre-built connectors.
Rollback flexibility: You can pause or revert anytime—no vendor lock-in.
In most cases, Syntasa augments your Databricks or Reverse ETL setup first, then gradually consolidates workflows for faster activation and stronger governance.