My main use case for Census is related to a customer that had well-modeled and reliable customer data in Amazon Redshift but struggled to make it usable in day-to-day business operations. Key customer attributes and segments were needed in CRM and marketing tools, yet the data was pushed through manual exports or custom-built pipelines, creating delays and inconsistencies. As a result, CRM data was often outdated, marketing audiences required engineering support to be updated, and any change in business logic led to slow release cycles. Census was introduced as a reverse ETL layer to directly sync curated warehouse models into operational systems. This enabled automated, incremental data activation without custom code, keeping business tools aligned with the latest warehouse data. By adopting Census, the customer eliminated manual processes, reduced engineering effort, and turned the data warehouse into a single operational source of truth for customer data.
What is our primary use case?
What is most valuable?
Census has been very useful to us as a reverse ETL layer. We have reduced engineering efforts, which has translated into tens of thousands of euros per year in avoided development and maintenance costs, faster and more accurate customer activation, and other improvements that we achieved using Census.
Census is faster than our previous approach. Census has enabled faster activation of analytic data into CRM and marketing platforms in our case. Another key benefit is the elimination of manual exports and custom integration scripts, consistent customer data across analytical and operational systems.
The best feature that Census offers is that it works directly on top of the data warehouse, preserving a single source of truth. Another valuable feature is that Census provides excellent support for CRM, marketing, and customer-facing platforms.
Census creates a single source of truth by making the data warehouse the only place where business logic and data definition live and by ensuring that all downstream operational systems are consumers, not owners of data. All customer attributes, metrics, and segments are defined once, directly in the data warehouse, using SQL and existing data models.
Census was useful for us because of the fast time to value, as new syncs can be configured and deployed quickly without custom development. It also ensures that data models are clean and well-defined in the data warehouse after we start using Census.
Based on delivery metrics and team feedback, the benefit is that the time to activate new use cases was reduced from weeks to days. Engineering effort dedicated to reverse ETL pipelines decreased by sixty to seventy percent. Maintenance overhead for custom integration was almost completely eliminated, and marketing and growth teams accelerated experimentation cycles significantly. From a cost perspective, reduced engineering effort translated into tens of thousands of euros per year in avoided development and maintenance costs, and faster and more accurate customer activation improved campaign effectiveness and business responsiveness.
What needs improvement?
The areas for improvement are complex transformation logic because it must still be handled upstream in the warehouse, and cost predictability requires attention at high data volumes. Customization options can be limited for very specific edge cases.
For how long have I used the solution?
I have been using Census since 2024.
What do I think about the stability of the solution?
Census is really stable.
What do I think about the scalability of the solution?
Census is scalable for high-volume data. If you have a lot of data, it is scalable without a problem. I would advise paying attention to costs because if you have a lot of data, costs may increase.
How are customer service and support?
Although it is not my direct experience, I have a colleague that is in charge of that, and the experience was very good. There are no issues about that.
How would you rate customer service and support?
Positive
What other advice do I have?
Census proved to be a powerful enterprise-ready reverse ETL platform. I recommend that you start to use Census while ensuring data models are clean and well-defined in the warehouse. You can monitor syncs and data quality from the beginning and clearly define ownership between analytics, engineering, and business teams. Census delivers maximum value when the data warehouse is treated as the central operational data layer. I would confidently recommend Census to organizations looking to bridge the gap between analytics and operational systems on AWS. I would rate this product a nine out of ten.
Which deployment model are you using for this solution?
Public Cloud
