Fivetran’s recent acquisition of Census is a meaningful step toward streamlining how data moves through modern organizations. Rather than simply bolting on a new feature, this integration reflects a shift in thinking—from linear “pipelines” to more iterative, closed-loop systems.
In this model, data doesn’t just support decisions; it cycles back into the tools where decisions are made, enabling faster, more responsive operations.
In this blog, we’ll discuss that while businesses have always had the option to buy a reverse ETL tool like Census, the idea that Fivetran saw their product as incomplete without reverse ETL is an exciting development.
It reveals the idea that a data product should not just be used to generate directional pipelines for business insights via dashboards, but also to create self-improving feedback loops by delivering actionable data directly to operational systems. A movement toward less manual intervention and more automated, continuous data flow.
What is Census?
Census is the pioneer of reverse ETL – a solution to move curated datasets from the warehouse and land them directly into operational systems. While there are several competitors in the reverse ETL space, Census has taken a similar approach to Fivetran in its design and value proposition. With hundreds of prebuilt connectors, the simplicity of their product is the appeal – select the connector associated with your destination product, connect it to your warehouse, and begin the sync – settings are minimal, schema mapping is automated. Fivetran’s decision to acquire Census is a natural pairing of two companies that see the future of data as abstracting away the complexity of ingestion and exfiltration, and centralizing transformation in the warehouse.
Rethinking the Data Product: Beyond Pipelines
The combination of Fivetran and Census reflects an evolving view of what a data product is and what it should enable. For years, the typical architecture emphasized modularity: ingest data from source systems, transform it in the warehouse, and (optionally) sync it back to tools like CRMs or ad platforms. Each step was handled by a separate tool, often managed independently.
This modular approach offered flexibility, but it also introduced complexity, both technical and operational. Fivetran and Census helped simplify the ends of this workflow: data ingestion and data activation. Now, by bringing both into a single platform, the tools offer a more cohesive experience.
This isn’t a radical redefinition of the modern data stack, but it does streamline what has historically been a fragmented process. It also highlights a broader trend: the move toward systems where insights and actions are part of the same continuous loop, not separate workflows managed in silos.
What Changes with Fivetran + Census
By combining ingestion and activation into one platform, Fivetran is aiming to simplify the full data lifecycle:
- Fivetran ingests raw data from source systems
- That data is transformed and modeled in the warehouse (often using dbt in combination with native transformation features)
- Curated datasets are then sent back to operational systems via Census
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These systems act on the data, triggering emails, workflows, or alerts
- Fivetran ingests raw data from source systems
This looped approach may not be new in concept, but it becomes more attainable when the tooling is integrated, governed, and easy to manage. For many teams, this reduces the need to glue systems together with custom scripts or one-off connectors.
Example: Real-Time Decision-Making
Imagine a customer browsing an e-commerce site, placing an item in their cart, and then removing it after comparing prices. Traditionally, that behavior might get logged for later analysis but not acted on in the moment.
In this integrated approach:
The event is captured by Fivetran in near real time
A dbt model builds a profile of that customer’s purchasing behavior
The result is synced back to Salesforce via Census
Salesforce sends a discount offer
The customer reconsiders and completes the purchase
The difference isn’t just speed—it’s coordination. When ingestion, modeling, and activation work together, businesses can respond more targeted and timely.
Toward a Warehouse-Native CDP
This model also presents an alternative to traditional Customer Data Platforms (CDPs), which often replicate data pipelines already present in the warehouse. Many CDPs come with rigid templates, limited flexibility, and can introduce redundant logic across teams.
A warehouse-native approach gives teams more control and avoids unnecessary duplication. Rather than relying on a black box, teams can:
- Ingest data once with Fivetran
- Transform it in the warehouse
- Push it out to operational tools via Census
This not only saves time but also allows the business logic to live in one place, governed and versioned just like application code.
Conclusion – From Stack to System
The integration of Fivetran and Census doesn’t reinvent the modern data stack, but it does represent a step toward a more integrated and maintainable architecture. By reducing handoffs between tools and consolidating key functions, it becomes easier to build reliable, responsive data systems that support both analysis and action.
Bring your data stack into the future.
If your team is exploring how to modernize your data stack or better activate your data, phData can help. Our experts specialize in designing and implementing scalable architectures using tools like Fivetran and Census, bridging the gap between data engineering and business impact. Let’s build a data foundation that’s not just modern, but built to last.
