Target architecture
Layers, platform, data flows and responsibilities, aligned with your requirements.
Data & cloud
Data warehouse consulting and delivery
Build or modernize one reliable data foundation for reporting, analytics and AI.
In short
Ki4data designs, builds and modernizes data warehouses for mid-sized companies: target architecture, source integration, data models, quality rules and documented delivery for BI, analytics and later AI applications. A data warehouse pays off when the same metric differs between reports, reports are built by hand or every new source is expensive to connect.
What is a data warehouse?
A data warehouse is a central data foundation where data from several source systems is integrated, cleaned and modelled so that reporting and analysis rely on the same, validated metrics. It typically has layers for raw data, integrated core data and business-facing analysis models.
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Typical starting points
- The same metric shows different values in different reports.
- Reports are built manually from exports and spreadsheets.
- Connecting new data sources takes a lot of effort.
- An existing warehouse is slow, hard to maintain or tied to a platform being phased out.
What you receive
Integration concept
How each source is connected, loaded and monitored, and on what schedule.
Data models
Traceable models for metrics and analysis, including history where it is needed.
Quality rules
Automated checks for completeness, freshness and business plausibility.
Documented delivery
Approved data products for BI and analytics, with lineage and meaning described.
How we work
Assess
Capture sources, reports, metric definitions and bottlenecks.
Architect
Choose platform and modelling approach with a clear decision model.
Build
Connect sources, build models and automate quality rules.
Validate
Reconcile metrics against existing values with the business.
Hand over and operate
Documentation, training and an agreed operating model.
Decision model for the architecture
- Data types: structured data, documents, events
- Latency: daily loads or near real time
- Team skills: what your team can run and extend later
- Existing platforms and licences
- Expected operating effort and running costs
Prerequisites and limits
A good fit when …
- Several source systems need to be combined for reporting and analytics.
- Metrics need one binding, shared definition.
- Data should later also feed AI applications.
Another solution fits better when …
- There is a single source and a handful of reports; reporting directly on that source is often enough.
- Data Vault is one possible modelling choice, not the right default for every mid-sized project.
What drives effort and cost
Every project is scoped individually. These factors influence the effort most:
- Number and diversity of sources
- Number and complexity of metrics and data models
- Freshness and history requirements
- Migrating an existing warehouse versus building new
Frequently asked questions
Data warehouse, data lake or lakehouse?
Mainly a question of data types, analysis goals and team skills. For mostly structured data and reliable metrics, a warehouse is usually the most direct route; we decide using the criteria above, not trends.
Do we need Data Vault?
Not automatically. Data Vault pays off with many sources, frequent structural change and strict traceability requirements. For smaller landscapes a simpler model is often more economical.
Can we start with one area?
Yes. We often start with one business area and a few metrics, to test architecture and quality rules in real use early on.
Snowflake or Microsoft Fabric?
There is no universal winner. Fabric combines OneLake, data engineering, warehouse and Power BI in the Microsoft ecosystem; Snowflake is a standalone cloud data platform with its own AI functions. Existing licences and tools, team skills, data types and expected operating effort decide.
Sources and vendor documentation
Related services
Data migration for mid-sized companiesMove data off legacy systems in a planned, validated way, with a rollback plan.RAG solutions for enterprise knowledgeTurn enterprise knowledge into answers with sources, permissions and measured quality.
Next step
In a first conversation we clarify your situation, goals and a sensible starting point. Afterwards you receive an assessment of what a project could look like.
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