Data Warehousing with Microsoft Fabric

With Microsoft Fabric, a new era of data warehousing begins. The platform unifies data integration, data engineering, data science, reporting, and governance within a single cloud environment — fully managed, highly scalable, and deeply integrated with Power BI.

While classic data warehouse architectures often require separate systems for ETL, storage, and analytics, Microsoft Fabric brings everything together. All data is stored centrally in OneLake, Microsoft’s universal data storage layer, and can be used directly across multiple workloads — without complex data movement or duplicate copies. This enables a consistent data architecture that combines transparency, speed, and cost efficiency.

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Use Cases

  • Modernization of existing SQL Server data warehouses
  • Establishment of central cloud data platforms for BI, reporting, and advanced analytics
  • Regulatory analytics and risk reporting in banking and insurance
  • Customer segmentation and sales analytics in retail and e-commerce
  • IoT data analytics and process optimization in logistics and industry
  • Data collaboration across organizational boundaries using OneLake sharing

Architecture and Principles

Comeli explaining the architecture and principles of Microsoft Fabric with OneLake and integrated workloads.

OneLake — the central data foundation

OneLake acts as an enterprise-wide data store — comparable to OneDrive for structured, semi-structured, and unstructured data. It stores structured, semi-structured, and unstructured datasets in an open Delta/Parquet format.

Workloads — specialized services within Fabric

All workloads access the data in OneLake directly:

  • Data Factory (data integration and pipelines)
  • Data Engineering (Spark notebooks, transformations)
  • Data Warehouse (SQL-based DWH)
  • Data Science (ML models in Python and R)
  • Power BI (reporting and visualization)
  • Data Activator (real-time monitoring and triggers)

Because all components operate on the same storage layer, redundant copies, exports, or synchronization mechanisms are avoided.

Capabilities

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The following points provide an overview of key technical capabilities and platform functions in the context of storage, processing, and analytics. The goal is an architecture where different analytical requirements can be implemented consistently — from ingestion and transformation through to delivery for reporting and business users.

  • Cloud-native data warehouse architecture with automatic scaling
  • Centralized data storage in OneLake — open, versioned, and secure
  • SQL-based warehouses for structured analytics
  • Lakehouses for flexible big data processing with Spark
  • Data Factory pipelines for ETL/ELT processes and automation
  • Direct integration with Power BI — no intermediate layers required
  • Support for Python, Spark, R, T-SQL, and DAX
  • Role-based security (RBAC) via Microsoft Entra ID
  • Versioning, governance, and monitoring via workspaces
  • Seamless connectivity to on-premises systems and Azure SQL Server environments

Services

As a partner for modern data solutions, we support organizations in using Microsoft Fabric effectively — from initial architecture design through to production use.

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Architecture and Strategy

  • Assessment of the current data landscape
  • Design of hybrid or cloud-native Fabric architectures
  • Setup of OneLake structures, workspaces, and access permissions
  • Governance and security concepts for data storage and access

Development and Implementation

  • Implementation of Fabric data warehouses and lakehouses
  • Design of ETL/ELT processes with Data Factory and Spark
  • Migration of existing SQL Server data warehouses to the cloud
  • Development of semantic models for Power BI
  • Integration of external sources (SQL, Oracle, SAP, REST, Parquet, JSON, etc.)

Analytics and Reporting

  • Connecting Fabric warehouses to Power BI datasets
  • Development of self-service analytics and dashboards
  • Configuration of Direct Lake mode for maximum performance

Coaching and Operations

  • Training and coaching for data engineers, BI developers, and analysts
  • Monitoring, cost optimization, and operational support
  • Introduction of DevOps and CI/CD processes for Fabric

Frequently Asked Questions on MS Fabric

This FAQ covers the topics most frequently discussed in consulting engagements and training sessions. Each answer is concise and refers to additional material where appropriate. If your question is not listed, please feel free to contact us.

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How does MS Fabric differ from a classic data warehouse?

Traditional data warehouse architectures often rely on separate systems for ETL, storage, and reporting. MS Fabric integrates these components into one platform, reducing data movement and simplifying governance.

Is MS Fabric suitable for existing SQL Server data warehouses?

Yes. Existing SQL Server data warehouses can be modernized and migrated into MS Fabric to benefit from cloud scalability and integrated analytics capabilities.

What role does OneLake play in MS Fabric?

OneLake is the central data store within MS Fabric. All workloads access the same data foundation, supporting consistency, traceability, and governance.

Does MS Fabric support hybrid architectures?

MS Fabric enables connectivity to on-premises systems and Azure SQL Server environments and can be integrated into hybrid data landscapes.