funnel.io is a marketing data hub. It collects data from ad platforms, CRM, and analytics, harmonises it into one schema, and makes it available in BigQuery, a BI tool, or directly as a report. Its sweet spot sits between source APIs and the warehouse.

When is funnel.io worth it?

From around ten ad platforms, or for teams without their own data-engineering capacity. For smaller setups, Supermetrics or a direct BigQuery import is often cheaper. Once you have a data team and need governance, Fivetran plus dbt catches up.

Is funnel.io GDPR-compliant?

funnel offers EU data processing and a data-processing agreement. Marketing reporting data is rarely highly sensitive, but the data flow still belongs documented. Compliance depends on the concrete setup, not the tool alone.

That's the real architectural question. The funnel UI is the fastest path but uses funnel-owned mapping. dbt on top of raw loads gives more control, versioned SQL models, and lineage. Which fits depends on your team and governance needs.

What does funnel.io cost?

The licence becomes a noticeable line item from mid volume and scales with sources and data volume. The architecture and setup run through our Audit Sprint and Build Sprint. We scope the exact effort in the audit.

funnel.io Setup & Integration

funnel.io is a reliable ELT tool that harmonises 500+ marketing sources before the data lands in BigQuery or Snowflake. The AI and BI layers above it get a clean, joined schema instead of raw connector dumps.

What is funnel.io?

funnel.io sits between the source APIs and your warehouse. It pulls data from 500+ sources, normalises it onto a shared schema, and forwards it, no-code and BigQuery-native. The strength: fast consolidation without your own ETL. The weakness: the licence gets expensive from mid volume, and once a data team is in house, Fivetran plus dbt wins on governance and lineage.

A detailed assessment with a pricing estimator and a head-to-head against Supermetrics and Fivetran lives in the Funnel.io review.

When funnel.io fits, and when it doesn't

A fit when:

you consolidate ten or more ad platforms
the team has no data-engineering capacity
a clean data foundation needs to stand up fast
BI and AI layers need one consistent schema

Less so when:

only a few sources are involved, where a direct import often suffices
a data team needs governance, lineage, and multi-domain coverage
the budget gets tight already at mid volume

funnel.io in the modern data stack

funnel.io is the harmonisation layer, not the warehouse and not the BI layer. A clean data flow looks like this:

Layer	Role	Example
Sources	raw data	Google Ads, Meta, TikTok, CRM
funnel.io	harmonisation	one schema, ELT
Warehouse	storage and modelling	BigQuery, Snowflake
BI / AI	analysis	dashboards, MMM, AI agents

From funnel, the consolidated data flows on to the warehouse, where dbt models and BI build on top.

What Datascale builds with funnel.io

We decide the architecture first, then build:

an assessment of whether funnel.io or Fivetran plus dbt is the right path
setup of the connectors and the target schema
mapping onto the warehouse model in BigQuery or Snowflake
connection to BI and, where it helps, to the AI layer
monitoring of data quality and cost control

The full picture lives in the Marketing Data Lakehouse. We don't sell funnel as an end in itself, only when it's the cheapest reliable fit for your case.

funnel.io: Marketing Data Harmonised Before the Warehouse