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Datenschaftler

Solution patterns

Understand the architecture without invented results.

These pages present representative system patterns and clearly mark where architecture knowledge ends and substantiated customer evidence would begin.

Architecture library

Traceable patterns for data platforms and applied AI

The collection connects a recognisable business need with architecture, pilot boundaries, prerequisites, and accountable operation.

01

From document sources to a traceable answer

The pattern connects ingestion, permissions, retrieval, answer generation, and operational data without claiming a specific customer outcome.

02

From engineering drawings to reviewable asset data

A representative pattern for extracting symbols, tags, and relationships from engineering diagrams with visible uncertainty and professional approval.

03

Lakehouse foundation for the Mittelstand.

Azure Data Lake Storage and Databricks organise raw, validated, and business-ready data in clearly separated layers.

04

Governed ERP and finance data mart.

SAP or Dynamics 365 data is extracted in a controlled way, reconciled with business definitions, and published as a traceable reporting layer for Power BI.

05

Document pipeline with mandatory approval.

Azure AI Document Intelligence captures orders or invoices, rules validate the content, and uncertain cases remain subject to human approval.

06

Permission-aware enterprise search with citations.

Azure AI Search and Azure OpenAI provide grounded answers while Microsoft Entra ID and document permissions constrain the retrievable context.

07

Condition monitoring and predictive maintenance on Databricks.

Time series, maintenance data, and asset context are connected in the lakehouse, versioned with MLflow, and provided to maintenance as reviewable indicators.

08

Data governance and lineage as a dependable baseline.

Microsoft Purview and Unity Catalog connect inventory, classification, roles, lineage, and audit for selected data domains.

Transfer

What would this pattern look like in your system landscape?

We assess data sources, permissions, user process, and operational requirements together.