Data Lakehouse Development with Microsoft Fabric
A modern data lakehouse combines the flexibility of a data lake with the structure and performance of a data warehouse. With Microsoft Fabric, we design scalable and extensible architectures that efficiently process structured and unstructured data and make it available for analytics.
Our solutions are based on a clearly defined technical architecture that integrates data integration, transformation, governance, and analytics within a single platform. This creates a consistent foundation for all data-driven applications across the organization.
Our Services
- Design and implementation of lakehouse architectures using Microsoft Fabric OneLake
- Development of data warehouse structures with Synapse Data Warehouse
- Implementation of Delta tables, Parquet files, and SQL endpoints
- Orchestration of ETL/ELT processes using Data Factory pipelines
- Automation and monitoring with Fabric Dataflow Gen2
- Python- and SQL-based transformations in notebook environments
Technologies
Microsoft Fabric is an integrated analytics platform that brings together all data and analytics capabilities within a shared environment. It combines data engineering, data warehousing, data science, and business intelligence in a cloud-based solution.
Through the close integration of OneLake, Synapse, Data Factory, Power BI, and machine learning capabilities, Fabric enables end-to-end data processing – from ingestion and transformation to storage, analytics, and reporting.
For development teams, Fabric provides a unified working environment with Git integration, notebooks, Delta tables, and SQL endpoints. This enables the efficient implementation of scalable, reusable, and automated data analytics solutions.
Lakehouse Architecture
A Microsoft Fabric lakehouse architecture combines the openness and scalability of a data lake with the structured analytical capabilities of a data warehouse.
It enables the storage and processing of structured, semi-structured, and unstructured data within a common format (e.g., Delta tables).
Analytics workloads, machine learning models, and BI queries access the same consistent data foundation – without redundant data silos.
Data Warehouse
The data warehouse is the relational analytical database component within Microsoft Fabric.
It enables the storage, modeling, and querying of large volumes of structured data using SQL.
Thanks to its cloud-native architecture, resources can be scaled flexibly, loading processes can be automated, and data can be made available for analytics in near real time.
Data Factory and Pipelines
Microsoft Data Factory is the integration and orchestration service within Fabric.
Pipelines define, execute, and monitor data flows between sources and destinations.
They control ETL/ELT processes, automate data loading workflows, and support complex dependencies, scheduling, and error handling within data workflows.
Fabric Dataflow Gen2
Fabric Dataflow Gen2 represents the next generation of Power Query-based data transformation within Microsoft Fabric.
It enables the extraction, transformation, and storage of data in OneLake – fully integrated with Data Factory.
Dataflows Gen2 can be used for reusable transformations, integrated into pipelines, and directly connected to Power BI datasets.
Frequently Asked Questions on Data Lakehouse Development with Microsoft Fabric
This FAQ addresses 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.
When is a data lakehouse approach appropriate?
A lakehouse architecture is suitable when structured and unstructured data need to be processed together and multiple analytical workloads (reporting, data science, SQL analytics) operate on the same data foundation.
What is the role of the OneLake data platform?
The OneLake data platform is the central storage layer within data lakehouse development using Microsoft Fabric. It ensures consistent data storage and shared access across different services.
How are ETL and ELT processes implemented with Microsoft Fabric?
ETL and ELT processes in Microsoft Fabric are implemented using Data Factory pipelines as well as notebook-based transformations with Python or SQL. ETL transforms data before loading, while ELT transforms data after loading into the target system. Both approaches can be orchestrated and monitored within the Fabric architecture.
What is the function of Synapse Data Warehouse within Fabric?
Synapse Data Warehouse Fabric serves as the relational analytics component of the platform. It enables structured modeling of large datasets using SQL and provides high-performance querying capabilities for reporting and analytical purposes.