Data Engineering in Microsoft Fabric: An Overview

Data engineering plays a crucial role in the modern data-driven world. It involves designing, building, and maintaining infrastructures and systems that enable organizations to collect, store, process, and analyze large volumes of data. Microsoft Fabric, a comprehensive analytics solution, offers a robust platform for data engineering. This blog post will provide a detailed overview of data engineering in Microsoft Fabric.

What is Data Engineering in Microsoft Fabric?

Data engineering in Microsoft Fabric enables users to design, build, and maintain infrastructures and systems that allow their organizations to collect, store, process, and analyze large volumes of data. Microsoft Fabric provides various data engineering capabilities to ensure that your data is easily accessible, well-organized, and of high-quality.

From the data engineering homepage, users can perform a variety of tasks:

  • Create and manage your data using a lakehouse
  • Design pipelines to copy data into your lakehouse
  • Use Spark Job definitions to submit batch/streaming jobs to Spark clusters
  • Use notebooks to write code for data ingestion, preparation, and transformation

Lakehouse Architecture

Lakehouses are data architectures that allow organizations to store and manage structured and unstructured data in a single location. They use various tools and frameworks to process and analyze that data. This can include SQL-based queries and analytics, as well as machine learning and other advanced analytics techniques.

Microsoft Fabric: An All-in-One Analytics Solution

Microsoft Fabric is an all-in-one analytics solution for enterprises that covers everything from data movement to data science, real-time analytics, and business intelligence. It offers a comprehensive suite of services, including data lake, data engineering, and data integration, all in one place.

Traditionally, organizations have been building modern data warehouses for their transactional and structured data analytics needs and data lakehouses for big data (semi/unstructured) data analytics needs. These two systems ran in parallel, creating silos, data duplicity, and increased total cost of ownership.

Fabric, with its unification of data store and standardization on Delta Lake format, allows you to eliminate silos, remove data duplicity, and drastically reduce total cost of ownership. With the flexibility offered by Fabric, you can implement either lakehouse or data warehouse architectures or combine these two together to get the best of both with simple implementation.

Data Engineering Capabilities in Microsoft Fabric

Fabric makes it quick and easy to connect to Azure Data Services, as well as other cloud-based platforms and on-premises data sources, for streamlined data ingestion. You can quickly build insights for your organization using more than 200 native connectors. These connectors are integrated into the Fabric pipeline and utilize the user-friendly drag-and-drop data transformation with dataflow.

Fabric standardizes on Delta Lake format. Which means all the Fabric engines can access and manipulate the same dataset stored in OneLake without duplicating data. This storage system provides the flexibility to build lakehouses using a medallion architecture or a data mesh, depending on your organizational requirement. You can choose between a low-code or no-code experience for data transformation, utilizing either pipelines/dataflows or notebook/Spark for a code-first experience.

Power BI can consume data from the Lakehouse for reporting and visualization. Each Lakehouse has a built-in TDS/SQL endpoint, for easy connectivity and querying of data in the Lakehouse tables from other reporting tools.


Microsoft Fabric is a powerful tool for data engineering, providing a comprehensive suite of services and capabilities for data collection, storage, processing, and analysis. Whether you’re looking to implement a lakehouse or data warehouse architecture, or a combination of both, Fabric offers the flexibility and functionality to meet your data engineering needs.

This blogpost was created with help from ChatGPT Pro 

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