Attribution & Analytics
Census
Census is a data activation and reverse-ETL platform that operationalizes the warehouse by syncing trusted data into business tools. Built around a data-engineering workflow, it lets teams sync models from Snowflake, BigQuery, Databricks, or Redshift to CRMs, ad platforms, support desks, and marketing tools, with versioning, observability, and dbt integration. It emphasizes reliability and governance so revenue, marketing, and operations teams act on the same metrics defined once in the warehouse, rather than exporting CSVs or maintaining brittle point-to-point integrations.
What it does
Census operationalizes warehouse data by syncing it into the tools where teams work, with a strong bias toward reliability and engineering rigor. It connects to Snowflake, BigQuery, Databricks, and Redshift, maps modeled tables to fields in destinations like Salesforce, HubSpot, ad platforms, and support systems, and keeps those records continuously in sync. It integrates tightly with dbt so the metrics defined in transformation models flow directly into activation, and adds observability, diffing, and alerting so syncs fail loudly rather than silently. Marketing teams use it to push audiences and conversion data to ad platforms, while sales and operations teams enrich CRM records, all from a governed single source of truth instead of manual exports.
Where it fits
Census operates at the activation layer of the modern data stack, moving governed warehouse metrics into the operational tools that marketing, sales, and support run on.
Core features
- Reverse-ETL syncs from major cloud warehouses to business tools
- Native dbt integration for model-driven field mapping
- Sync observability with diffs, logs, and failure alerts
- Destinations spanning CRM, ad platforms, support, and marketing
- Audience and segment builder on warehouse data
- Role-based access and change governance
Best for
- Data engineering teams standardizing activation on dbt models
- RevOps teams syncing enriched records into CRMs
- Marketers pushing governed audiences to ad platforms
Beginner notes
- Lean on its dbt integration so destination fields trace back to documented, tested models rather than ad-hoc SQL.
- Start with a low-risk sync and confirm field mappings before activating data that drives customer-facing actions.
- Use the observability and diff features to catch upstream model changes before they corrupt a live destination.