Lakehouse Architecture
A central lakehouse replaces the patchwork of data warehouse, data lake and Excel exports.
A lakehouse architecture built on Delta Lake or Microsoft Fabric brings structured and unstructured data together on one platform. Analytics, BI and AI draw on the same consistent data instead of working in silos. This cuts duplicate work and lays the technical foundation for every future data project.
- One reliable data foundation for all departments.
- Less friction between IT, BI and business units.
- Faster turnaround on new analytics requests.
data duplicates
faster analytics
to pilot operation
infrastructure cost
Calculated from license and operating costs of legacy systems versus the consolidated platform.
- 01
Assessment
We map existing data sources, systems and access patterns.
- 02
Target architecture
We design the lakehouse structure with bronze, silver and gold layers.
- 03
Migration
We migrate core datasets step by step without operational downtime.
- 04
Integration
We connect BI tools, data science environments and applications.
- 05
Operations & scaling
We hand over monitoring, cost control and scaling rules.
Steps
Data sources
Stakeholders
From first data access to production – every step delivers a tangible interim result.
ERP data
Order, financial and inventory data from the ERP system.
Machine data
Sensor and status data from production and equipment.
CRM data
Customer and sales data from the CRM system.
Documents
Contracts, reports and logs in unstructured form.
Log and event data
System and application logs from the IT landscape.
External data
Market, weather or supplier data from third-party sources.
IT leadership
Fewer systems to run and maintain.
Business units
Faster access to reliable metrics.
Data science team
Direct access to clean, current data.
Executive management
Transparency on data cost and value.
One reliable data foundation for all departments.
Less friction between IT, BI and business units.
Faster turnaround on new analytics requests.
Lower costs through system consolidation.
A stronger foundation for AI and automation projects.
With a solid data foundation this use case gets faster, cheaper and far more stable.
An existing platform significantly shortens migration time.
A unified access model avoids duplicate effort.
Scalable compute resources reduce operating costs.
Standardized pipelines speed up new use cases.
Data quality & observability
The lakehouse becomes the central point for quality checks.
MLOps & model operations
Training data for models is already prepared.
Business intelligence
Dashboards connect directly to the gold layer.
Data Integration from ERP & MES
Automatically merge ERP and MES data instead of exporting and reconciling manually.
Streaming & Real-Time Data
Instead of daily batch runs, relevant metrics are available within seconds.
Data Modeling & Semantic Layer
A shared semantic layer ensures 'revenue' means the same thing everywhere.