Modern Data Stack & Composable CDP
We build your Marketing Data Lakehouse. Instead of expensive all-in-one CDPs we use your own cloud (BigQuery/Snowflake) as the core and push data into your marketing tools via Reverse-ETL.
- BigQuery
- Snowflake
- dbt
- funnel.io
- Hightouch
- Census
Collect
funnel.io
500+ marketing sources, partner setupIngest
dbt + Airflow
ELT pipelines, daily orchestration, monitoringStore
BigQuery EU
europe-west3 Frankfurt. CMEK optional for Schrems II hardeningTransform
dbt
Staging, marts, attribution logic, LTV calculationActivate
Hightouch / Census
Reverse-ETL into ads, CRM, Klaviyo, HubSpot
EU data residency guaranteed, no US-cloud detour. CLOUD Act hardening covered in the FAQ below.
What we build
GA4 has a data sampling problem. Meta Ads reports different conversions than GA4. The shop has its own revenue numbers. The CRM knows customer lifetime value but doesn't share it with anyone. And Excel ties all four together, manually, once a week, with error potential in every row.
The Marketing Lakehouse solves this structurally: all relevant data lands in one central EU-compliant data lake (BigQuery or Snowflake), gets normalised and enriched, and is ready for dashboards, ML models, and marketing activation.
The result: a single reliable data source for the whole company. No more debate over which system has the right numbers.
Who it's for
E-commerce with siloed data
Data in shop, ad systems, CRM, and customer service that never connect. LTV analysis and churn prognosis on the roadmap, but the data foundation is missing.
B2B with sales cycle
Marketing, CRM, and finance never show the same numbers. CAC and LTV aren't internally calculable because data is locked in silos.
Pre-BI-buildout teams
Know that Power BI or Tableau need scalable data sources, and don't have them today.
ML & AI teams
Planning ML models or AI-powered segmentation and need structured first-party data in clean schema as a foundation.
Deliverables
Joint engagement with clear ownership split, datascale delivers architecture, pipelines, and activation logic, Saloid the EU cloud infrastructure.
Data architecture
- Current-state analysis of all relevant data sources (completeness, quality, freshness)
- Target architecture document: which data goes where, which schema, which granularity
- Tool decision BigQuery vs. Snowflake with reasoning for the concrete context
Data pipelines
- ELT pipelines for all relevant sources (funnel.io + custom connectors for shop, CRM, finance)
- Data transformations in dbt: normalisation, enrichment, business logic
- Daily automatic refresh with monitoring and alerting on failures
Marketing activation
- Attribution model (data-driven instead of last-click, on your own data)
- LTV calculation based on historical transaction data
- Audience segmentation for reverse ETL (data back into Google Ads, Meta, Klaviyo, HubSpot)
- Predictive audiences when ML resources are available
Reporting & handover
- Data Studio, Power BI, or Tableau dashboard on top of the data lake
- Full documentation of the data model for internal BI teams
- Handover to internal data team or ongoing operations as a retainer
- 30-day post-launch support
Tools & stack
Data lake & hosting
- BigQuery (preferred, EU region europe-west3 / west4)
- Snowflake (EU region) as alternative
- EU hosting via Saloid for custom requirements
- Data residency guaranteed within the EU
Pipelines & transformation
- funnel.io (partnership), covers 500+ marketing data sources
- dbt for data transformations, normalisation, business logic
- dbt Cloud or Airflow depending on complexity
- Custom connectors for shop, CRM, finance when not via funnel.io
Activation & reporting
- Hightouch or Census for reverse ETL (data back into ad systems, CRM, Klaviyo)
- Data Studio for fast standard reporting
- Power BI for Microsoft stacks
- Tableau for Salesforce stacks or existing license
- 04:12:01 stg_funnel__meta_ads
- 04:12:18 stg_funnel__google_ads
- 04:12:34 stg_shopify__orders
- 04:13:02 mart_attribution_daily
- 04:13:21 mart_audience_high_ltv
- 04:13:45 hightouch.sync.audience_high_ltv → meta_ads
- 04:13:58 hightouch.sync.audience_high_ltv → klaviyo
Engagement depths
Three depths. Clear scopes.
No retainer trap.
Audit Sprint
We audit what's wrong. Report + prioritised action plan.
plus statutory VAT · fixed price for a clearly bounded scope
Included in the fixed price
- 1 domain
- 1 analytics property
- 1 tag manager / tracking setup
- 1 CMP
- up to 5 core conversions
- 10 working days
- PDF report + 90-min walkthrough
What you get
- Full analysis of your existing setup
- Prioritised report with concrete action items
- Walkthrough call with the team (90 min)
- No follow-up contract, no retainer obligation
When it fits
When the setup works but the numbers are being argued about internally. Or you're unsure what from a UA→GA4 migration still holds.
For e-commerce, multiple domains or App + Web: Audit Sprint Plus, €3,900 net fixed price. Bonus: 50 % of the Audit Sprint credits toward a Build Sprint commissioned within 30 days.
Request an Audit SprintBuild Sprint
Fresh build or restructure of a tracking setup.
plus statutory VAT · final fixed price after scope definition
Typical scope
- 1 domain (multi-domain on request)
- 1 analytics property (GA4 or Piwik PRO)
- server-side container (Stape or own cloud)
- 1 CMP with Consent Mode V2
- up to 15 events / conversions
- 4–8 weeks delivery
- Blueprint, QA sign-off, handover docs
What you get
- Measurement Blueprint for your dev team
- GTM + server-side setup incl. CMP integration
- Full QA against the blueprint with sign-off
- Handover docs + 30-day post-launch support
When it fits
When analytics is structurally broken and fixing it in-flight costs more than a clean rebuild.
Managed Evolution
Ongoing partnership. Analytics as a product, not a one-off project.
plus statutory VAT · monthly cancellation after the minimum term
Included in the monthly price
- up to 3 domains under active care
- GA4 + server-side stack maintenance
- monthly roadmap + sprint planning
- QA on every release deploy
- Slack channel, < 4 h response (Mon–Fri)
- monthly report + executive summary
- 3-month minimum, then monthly
What you get
- Monthly development + feature rollouts
- Ongoing QA on every deploy
- Executive reports + dashboard evolution
- Slack support with guaranteed response times
When it fits
When analytics has to grow with you (new campaigns, new products, new data sources) and you don't want to build that team internally.
All prices net, plus statutory VAT. For companies in Germany, Austria and Switzerland.
Full-Cycle Delivery, who does what
Datascale owns
- Marketing KPI definitions and data-source inventory
- funnel.io configuration (partnership) for marketing data
- Reporting layer: Data Studio / Power BI / Tableau on the lake
- Business logic for attribution, LTV, ROAS / POAS calculation
Saloid delivers
- Data architecture design (BigQuery / Snowflake, EU region)
- ELT pipelines: dbt transformation layer, orchestration
- Reverse ETL (Census / Hightouch for audience activation)
- Cloud infrastructure and monitoring
- BigQuery EU
- funnel.io
- dbt
- Data Studio / Power BI
- Census
Retail client. Unified shop, Meta Ads, Google Ads, and CRM in BigQuery EU. POAS dashboard replaces a manual ROAS report.
Saloid: Data Engineering & Analytics Implementation-
What is the difference between a data warehouse and a data lake?
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.
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Do we really need a data lake, or is funnel.io alone enough?
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.
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Can we host BigQuery in the EU?
Yes. BigQuery offers EU regions (europe-west3 Frankfurt, europe-west4 Netherlands). Datascale configures all projects in EU regions by default. No data leaves the EU.
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How do you mitigate the US CLOUD Act in a GCP-based architecture?
BigQuery is configured exclusively in an EU region (europe-west3 Frankfurt or europe-west4 Netherlands). Data residency is contractually guaranteed 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.
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What does a Composable CDP cost compared with an all-in-one CDP?
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 as a Build Sprint from €7,500 net. 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.
Next step
Marketing Data Lakehouse: architecture conversation.
Strategy call about BigQuery/Snowflake architecture and reverse-ETL activation. Full-cycle implementation together with Saloid.