When we talk about warehouse-native activation, we’re not just describing a product capability. We’re describing how we operate.
Fivetran Activations powers many of the same kinds of workflows internally that our customers use to drive marketing, sales, and operational outcomes. From platform integrations to infrastructure troubleshooting to product prioritization, we rely on the same pattern we recommend to customers: centralize data in a data warehouse or lakehouse, model it with analytics, and activate it directly into the tools where work happens.
Below are three real production use cases that show how Fivetran uses Fivetran Activations every day — not in theory, but in live systems that support billing, engineering, sales, and product teams.
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Use case 1: Using Fivetran Activations to integrate Census
In order to integrate Fivetran’s new acquisition — Census (now Fivetran Activations) — our systems engineering team built a critical workflow using Fivetran Activations.
When Census customers — such as the former Census company itself — link their existing account to their Fivetran account to enable unified billing, that process initiates inside the Census product. We use Fivetran to sync subscription and account data from the Census production database into BigQuery using the standard Fivetran data connectors. In BigQuery, we model the data and associate it with the appropriate customer accounts.
From there, Fivetran Activations moves that data directly into Salesforce and then Fivetran’s production environment. From Fivetran’s production database, the cycle can start again: data integration into a data warehouse, transformations, then data activation. The result is a seamless, automated loop that updates billing and account information without manual handoffs or custom scripts.
This production use case demonstrates that Activations extends far beyond traditional sales and marketing workflows. It powers internal operational processes just as effectively, proving that the same warehouse-native activation model our customers rely on also underpins our own platform integrations.
Use case 2: Powering Account Explorer
Activations isn’t just part of our integrations — it also supports internal tools that help our teams operate and support customers more effectively.
One example is Account Explorer, an internal application used by sales engineers to troubleshoot customer environments. Account Explorer visualizes VPN and networking flows tied to customer connections, giving internal teams clear visibility into how data is moving at the network layer. It helps answer practical questions like where traffic is flowing, whether connections are behaving as expected, and where potential bottlenecks or failures might be occurring.
Under the hood, this workflow is simple but powerful. Data is modeled in BigQuery and then synced directly into Postgres, which powers the transactional application behind Account Explorer. Activations handles that final step — moving the curated data from the warehouse into the operational database — without requiring custom pipelines or ongoing maintenance.
This use case illustrates something important: the same warehouse-native activation model our customers use to drive business outcomes is also helping our own teams move faster and troubleshoot smarter.
Use case 3: Managing customer requests at scale
Another internal example of Activations at work is the Feature Request App, built by our product team to manage customer feedback at scale.
The app aggregates feature requests from systems like Zendesk and Jira, centralizes them in BigQuery, and uses analytics — and increasingly AI — to categorize, prioritize, and triage requests across the product landscape. Instead of relying on scattered tickets and manual sorting, the team gets a structured, data-driven view of what customers are asking for and how those requests align with roadmap priorities.
Like many modern data workflows, the heavy lifting happens in the warehouse. BigQuery serves as the analytical backbone, where requests are enriched, grouped, and scored. But analytics alone isn’t enough — teams need that insight inside the operational tool where decisions are made. Activations bridges that final step, syncing the curated data from BigQuery into Postgres to power the application interface used by the product team.
Together, BigQuery, AI, and Activations create a repeatable pattern: ingest broadly, analyze centrally, and activate directly into the tool that drives action. It’s the same architecture many of our customers are building — and one we’re continuing to expand internally as new workflows emerge.
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