Data integration and enterprise analytics
Anonymized reconstructionData architecture, Integration, BI
Integrating sources, building a data platform and enabling analytics for decision-making.
- Operational sources
- Integration
- Data lake / warehouse
- Semantic model
- Power BI
Context and problem
The information needed to operate and decide was spread across systems that shared neither definitions nor update cycles.
Diagnosis and constraints
Heterogeneous sources had to be consolidated, data quality and lineage ensured, and the storage layer separated from the consumption layer.
Solution and architecture
A layered architecture with a data lake, data warehouse and data marts; version-controlled transformations with dbt and semantic models for analytics in Power BI.
Role and leadership
In progressResponsibilities, decisions made and how execution was guided.
Results and lessons
In progressVerifiable results and lessons learned.
What it shows
Architecture, design decisions, data flows, quality, lineage and consumption capabilities.