A modern data platform design: ingestion, storage, modelling, serving, and governance
End-to-end design for a data platform: ingestion topology, storage strategy (warehouse vs lakehouse), modelling layer, serving surfaces, governance, and team topology — tuned to your scale and maturity.
Get Started — $29/moWhere teams get stuck
- Ingestion is bolted together with ad-hoc scripts that nobody owns
- Warehouse cost grows faster than query volume and nobody can trace why
- Semantic layer is inconsistent, so two teams report different revenue numbers
- Governance is treated as a compliance checkbox, not an operating mechanism
- Team topology (centralised vs mesh) is decided by who shouts loudest
What you walk away with
- Reference architecture tuned to your scale (small / medium / enterprise)
- Ingestion plan (batch vs CDC vs streaming) with tool-agnostic recommendations
- Storage strategy comparing warehouse, lakehouse, and hybrid with cost model
- Semantic / modelling layer proposal (dbt-style vs LookML-style)
- Governance operating model: ownership, access, data contracts
- Team topology recommendation tied to your org structure
How it works
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1
Describe current state
Sources, volumes, latency needs, key consumers, current tooling, and team structure.
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2
Run the Data Engineer
Produces the reference architecture and layer-by-layer recommendations.
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3
Layer Database Optimizer
Deep pass on the warehouse/lakehouse choice, cost modelling, and query patterns.
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4
Add governance
The AI Data Remediation Engineer handles data-quality contracts; the Automation Governance Architect covers the operating model.
Specialists that run this use case
Data Engineer
Design, build, and operate reliable data pipelines that deliver high-quality, analytics-ready data from raw sources to...
Database Optimizer
Design schemas, write queries, and tune indexes that perform correctly under load — eliminating N+1 patterns, enabling...
Backend Architect
Design and deliver scalable, secure, performant server-side systems — including service decomposition, database...
AI Data Remediation Engineer
Intercept anomalous data rows after deterministic validation, compress them into semantic clusters, generate...
DevOps Automator
Design and implement CI/CD pipelines, infrastructure as code, container orchestration, monitoring, and deployment...
Frequently asked questions
Does it recommend specific vendors?
Yes, with trade-offs. Recommendations are opinionated but include a "why not X" paragraph for each major alternative so you can defend the choice internally.