Ten years ago, a “proper” data stack meant a six-figure budget and a team of three. In 2026, an SME can have real-time BI for a few hundred euros a month, set up in two weeks. Here’s the stack we actually deploy — not a theoretical one.
The layers
1. Ingestion
What it does: move data from where it lives (CRM, ERP, Postgres, Stripe, Google Ads…) into one place.
What we use: Airbyte or Fivetran for managed connectors, n8n for anything weird. Cost for an SME: €50–€300/month.
2. Storage + compute
What it does: hold everything and let you query it fast.
What we use: BigQuery (pay-per-query, lazy-friendly) or Snowflake (more predictable billing). For teams already on AWS, Athena works. Cost: <€100/month until you’re at real scale.
3. Transformation
What it does: turn raw tables into business concepts (customers, orders, MRR).
What we use: dbt. It’s SQL with tests and documentation. Your CFO can read it. Cost: free (open source) or $100/month/seat for Cloud.
4. Activation + BI
What it does: get the insights into the tools people actually use.
What we use:
- Metabase or Looker Studio for dashboards.
- Hightouch or Census for reverse ETL — push clean data back into your CRM, Slack, etc.
- Occasionally a thin React app when an operator needs something richer than a dashboard.
5. The AI layer (optional, but this is where the leverage is)
Once you have clean modelled data in a warehouse, adding a semantic layer + an AI assistant that answers questions in natural language (“what drove MRR churn last month?”) is a weekend of work, not a project.
The full cost
For a 50–200 person company, all-in: €800–€2,500/month including tools and hosting. The ROI shows up in the first quarter because you stop paying people to copy-paste between spreadsheets, and you stop making decisions with week-old numbers.
What we tell clients to skip
- “Data lake first” — you don’t need one. Start with the warehouse.
- Kafka / streaming infrastructure — batch every 15 minutes is fine for 95% of use cases.
- Custom-built dashboards — Metabase is 80% of the value for 5% of the effort.
- Hiring a data engineer as your first data hire — hire an analytics engineer who can do dbt + modelling. The engineer comes later, if ever.
The gap between “we run on spreadsheets” and “we have real-time BI” used to be a two-year journey. For an SME that knows what it wants, it’s two weeks.