Your composable CDP and marketing data warehouse, plus governance

A classic data warehouse (Redshift, on-premise systems) is highly structured and optimised for SQL queries. A modern data lake or lakehouse (BigQuery, Snowflake) combines the flexibility of a lake with the query performance of a warehouse. For marketing use cases BigQuery is the better choice in most setups today.

funnel.io alone is sufficient for most marketing dashboards (→ Revenue Intelligence). A data lake becomes necessary when shop transaction data, CRM data, and marketing data need to be unified, when LTV or attribution models should be calculated, or when ML applications are planned.

Yes, BigQuery offers EU regions (europe-west3 Frankfurt, europe-west4 Netherlands). Datascale configures all projects in EU regions by default. All data stays in the EU region.

BigQuery is configured exclusively in an EU region (europe-west3 Frankfurt or europe-west4 Netherlands). Data residency is contractually assured by Google. Residual risk: the CLOUD Act targets US parent companies. For highly sensitive data we combine BigQuery encryption with CMEK (Customer-Managed Encryption Keys), optionally with an External Key Manager from EU vendors like Fortanix or Thales. The decryption key then sits outside CLOUD Act reach. For maximum sovereignty: Snowflake on AWS Frankfurt with the same External-Key setup, or an open-source lake on StackIT or IONOS.

An enterprise CDP (Segment, mParticle, Tealium) typically runs €80,000 to €250,000 per year, depending on MTU volume. Plus implementation. A composable setup on BigQuery EU sits in a different order of magnitude: BigQuery storage and compute together stay under €1,500 per month for most DACH mid-market companies, dbt Cloud Team plan from €100 per month, Hightouch Starter from €350 per month. Implementation runs as a Build Sprint at a fixed price set after the audit. Year-one total cost is typically €35,000 to €60,000. The biggest difference is not the price. It is data control: storage belongs to you, not to the CDP vendor.

Data quality measures individual values (completeness, format, range). Data reliability guarantees that a definition means the same thing across systems, and that automated tests keep it that way when someone changes a schema or connects a new source. Data quality is a snapshot; data reliability is a process.

No. We start with the sources that already exist. GA4, Plausible, CRM exports, ad APIs. If the audit shows a central layer is missing, that becomes a recommendation, not an upfront investment. Most reliability problems can be addressed without a finished lakehouse, as long as definitions and tests are properly documented once.

Someone internal, from the data or analytics team. We build the framework, document the processes, and onboard the first steward, but governance only works if it's owned internally. In our experience, externally-staffed steward roles don't last 12 months.

After a 2-week Audit Sprint the 3–5 most critical pipelines have alerts in place. The full build with data catalogue, RBAC model, and governance processes typically runs over 6 to 10 weeks, together with Saloid for the technical implementation.