What is Microsoft Fabric? Unraveling the Data Fabric Components

Velosio breaks down the power of Microsoft Fabric and its essential components in our latest blog, to unlock efficient workflows.

Table of Content

    Microsoft Fabric 101: Understanding Its Components

    Microsoft recently unveiled a groundbreaking analytics solution — Microsoft Fabric. Fabric is a comprehensive suite of services for data analytics, integrating everything from data movement to business intelligence. It’s not a single application; instead, it’s a group of complementary applications that work together to form a robust data analytics solution. We frequently get asked about what’s included in Microsoft Fabric, so here we cover each of the data fabric’s components and how you can weave them together for maximum gain.

    Threads of Innovation — Understanding Microsoft Fabric’s Components

    Below is a high-level list of the components that combine to form Microsoft Fabric.

    • Data Engineering

    At the heart of Microsoft Fabric lies its Data Engineering capability. This component offers a world-class Spark platform, enabling data engineers to perform large-scale data transformations and democratize data through the lakehouse. Integration with Data Factory allows for the efficient scheduling and orchestration of notebooks and Spark jobs.

    • Data Factory
      The Data Factory element of Microsoft Fabric merges the simplicity of Power Query with the robust capabilities of Azure Data Factory. It offers over 200 native connectors to facilitate seamless connections to data sources both on-premises and in the cloud​​​​.
    • Data Science
      The Data Science feature is a pivotal component, allowing the building, deployment, and operationalization of machine learning models within the Fabric ecosystem. It integrates with Azure Machine Learning, enhancing the capability to track experiments and manage models​​​​.
    • Data Warehouse
      Microsoft Fabric’s Data Warehouse experience offers unmatched SQL performance and scalability. It distinctively separates computing from storage, allowing for independent scaling. The use of the open Delta Lake format for data storage is another highlight of this component​​​​.
    • Real-Time Analytics
      Addressing the fastest-growing data category, Synapse Real-Time Analytics in Microsoft Fabric provides an exceptional engine for analyzing observational data from various sources, such as IoT devices and apps. This data, often semi-structured, is adeptly handled by this component​​​​.
    • Power BI
      As a leading Business Intelligence platform, Power BI within Microsoft Fabric ensures quick and intuitive access to data, aiding business owners in making informed decisions​​​​.
    • OneLake and Lakehouse
      A unique aspect of Microsoft Fabric is the integration of OneLake and lakehouse architecture. OneLake serves as a unified location for storing all organizational data, simplifying data management, and enhancing collaboration across various departments​​​​.

    Semantics Make Fabric Make Sense

    Above, we’ve listed the data fabric components, but you may be asking, “How do we make sense of Microsoft Fabric and actually put it to use in our organization?” The answer lies in semantics.

    A semantic layer in the context of data analytics is a critical component that bridges the gap between raw, technical data and the business users who need to access and understand this data for decision-making. It’s not an application. It’s a series of well-planned business processes and workflows designed to optimize how you use Microsoft Fabric (or any other complex platform).

    Think of a semantic layer as a translator. It takes complex data from various sources and transforms it into a business-friendly format, using terms and concepts that are familiar and meaningful to end users. It sits between the underlying data sources (like data warehouses or lakes) and the analytical tools or applications used by business professionals.

    One of the key functions of the semantic layer is to provide a consistent, unified view of data across an organization. This is crucial in environments where data is decentralized, and different departments may have multiple definitions or formats for the same data. For example, marketing, sales, and finance teams might refer to a business entity using different terms in their respective systems. A semantic layer helps by mapping these varied definitions to a single data entity, ensuring uniformity and clarity.

    A data fabric and a semantic layer complement each other in the context of a modern data architecture. While a data fabric provides a unified way to manage, access, and govern data across multiple sources, a semantic layer offers an abstraction that simplifies end-user data consumption. By combining both, organizations can create a seamless and efficient data infrastructure.

    Weaving Together Microsoft Fabric and a Semantic Layer

    Here’s an example of how an organization could use a semantic layer with Microsoft Fabric components to create a robust, scalable, and understandable data architecture.

    1. Data Ingestion
      Use Data Factory to ingest and move data from various sources (such as on-premises databases, SaaS applications, and other cloud-based systems) into a centralized storage system like Azure Data Lake Storage Gen2.
    2. Data Processing
      Process and transform the ingested data using Synapse Analytics. This could involve data cleansing, enrichment, or aggregation to make the data more useful for analysis.
    3. Data Fabric Implementation
      Implement Azure Purview as part of your data fabric. Azure Purview lets you discover, understand, and manage your data across on-premises, multi-cloud, and SaaS environments. It also helps you automate the discovery, classification, and management of sensitive data for compliance purposes.
    4. Data Storage
      Store the processed and enriched data in Azure Synapse Analytics, which serves as a data lakehouse, providing both a data lake’s scalability and a data warehouse’s structure.
    5. Semantic Layer
      Use Power BI to create a semantic layer on top of the data stored in Azure Synapse Analytics. Define relationships, calculations, and hierarchies within Power BI to create a user-friendly data model that abstracts the underlying technical details.
    6. Data Visualization and Analytics
      With the semantic layer in place, end-users can easily create reports and dashboards in Power BI to visualize and analyze data without needing deep technical expertise.
    7. Real-time Data Processing
      You can use Azure Event Hubs to ingest streaming data and Azure Stream Analytics to process it for real-time data processing. Then, you can store processed real-time data in the data lakehouse or another storage system, and the insights derived can be made available through the semantic layer in Power BI.

    The key to successful implementation is ensuring the semantic layer is user-friendly, aligns with your business terminology, and is flexible enough to adapt to your changing business needs. Collaboration between IT, data professionals, and business users is crucial throughout this process to ensure the semantic layer effectively translates technical data into meaningful business insights. Velosio’s Azure team is ready to help make this happen for you.

    Seamlessly Stitching Components: The Fabric of Data Analytics

    Microsoft Fabric is an all-in-one solution, weaving diverse components into a cohesive and integrated platform. Fabric offers small and mid-sized enterprises a streamlined and efficient approach to managing their data by integrating key components such as Data Engineering, Data Warehouse, and Real-Time Analytics. For Velosio’s clients, this means access to a state-of-the-art analytics tool that simplifies the data management process and unlocks new potential for data-driven insights and decision-making.

    As a premier Microsoft VAR, Velosio is equipped to lead a seamless integration of Microsoft Fabric for your organization. Contact us with your questions.