Working with Fabric, bringing multiple services from diverse vendors together is unnecessary. Instead, you have a highly integrated, comprehensive and easy-to-use product designed to simplify your analytical needs.

Fabric is built on a Software-as-a-Service (SaaS) foundation, elevating simplicity and integration to a new level.

Fabric brings together expertise such as Data Engineering, Data Factory, Data Science, Data Warehouse, and Real-Time Analytics on a shared SaaS basis. For this purpose, Microsoft Fabric uses new and existing components of Power BI, Azure Synapse, and Azure Data Factory into a single integrated environment.

Components of Fabric in Azure

• Data Engineering – provides a premier Spark environment with great data automation experiences, empowering data engineers with the ability to perform large-scale data transformations and democratise data across the Lakehouse. Microsoft Fabric Spark’s integration with Data Factory allows you to code and orchestrate Spark notebooks and jobs.

• Data Factory – combines the ease of Power Query with the scale and power of Azure Data Factory. Over 200 native connectors can be used to connect to on-premises and cloud data sources.

• Data Science – allows you to aim to create, deploy, and run machine learning models effortlessly within your Fabric experience. Integration with Azure Machine Learning provides experiment tracking and built-in model logging. Using it, data scientists can enhance organisational data with predictions and forecasting, enabling business analysts to integrate such predictions into BI reporting. In this way, descriptive insights can be moved to a predictive view.

• Data warehouse – drives market-leading SQL performance and scale. Completely separates compute from storage, which allows independent scaling of both components. Furthermore, it stores data natively in the open Delta Lake format.

• Real-time analytics – analytics is based on data collected from a variety of sources, such as applications, IoT devices, human interactions, and many more. This data is usually semi-structured in formats such as JSON or text. Arriving in large volumes, with changing schemas. As a result, these characteristics make traditional data warehousing platforms difficult to work with.

• Power BI – is the leading Business Intelligence platform in the business. By ensuring that business owners can access all Fabric data quickly and intuitively to make better data-driven decisions.

Azure data Fabric architecture

OneLake is the central denominator which connects the different fabric services: it serves as the common storage location for ingestion, transformation, real-time insights, and Business Intelligence visualisations:

Fabric integrates five existing and enhanced workloads and adds a new workload in a Power BI-based portal: Synapse Data Science, Synapse Data Warehousing, Synapse Data Engineering, Synapse Real Time Analytics, Data Factory and Power BI, so Fabric can be summarised as follows:

These tools can be found in Microsoft Azure Sync Fabric allowing a more user-friendly and convenient flow of information.

Implement Azure data mesh architecture with domains in Fabric easily

Due to the massive growth of data that companies face today, federated models are beginning to be used depending on the business’s need to manage and organise this data set logically and efficiently. This has been a big change from traditional architectures governed and managed in highly centralised ways. This federated data architecture is referred to as data mesh.

Microsoft Fabric’s data mesh architecture provides support within domains and allows data consumers to filter and discover content by domain. Additionally, the architecture supports federated governance, which means that some governance currently handled at the tenant level can be delegated to domain-level control, letting each business unit/department define its own rules and constraints according to its specific business needs.

Consequently, customers have the key tools they need to structure their tenant data estate in a way that allows them to control their own data mesh.

Data mesh is a central technical pattern that also demands organisational changes. The advantages of a data mesh approach are achieved by implementing multidisciplinary teams, which publish and consume data products.