Overview of Microsoft Azure Fabric
Introduction
The world is in the information age, where sensors dominate life, industrial, and business activities. The sensors collect large volumes of data that can be analyzed for various benefits at different levels. For example, businesses can harness Big Data to enable relevant digital transformations and gain a competitive advantage. Big Data-based analytics is becoming increasingly dominant with the rapid progress and proliferating uptake of artificial intelligence (AI) technologies. AI systems, such as language models and generative AI, are particularly important for businesses, helping reinvent how workers spend their time and the amount of insights that can be obtained from data. Enterprises need steady supplies of high-quality data and inferences from highly integrated analytics systems to achieve optimal outcomes from Big Data. However, most businesses depend on analytics systems with multiple specialized and disconnected services, increasing complexities and costs. Microsoft Fabric addresses these concerns by offering an end-to-end, unified analytics platform.
The Azure Service Fabric is Microsoft’s approach to a microservice architecture. It is fundamentally a distributed systems platform that simplifies packaging, deploying, and managing reliable and scalable microservices and containers (Sahay, 2020). The analytics platform also solves the extensive drawbacks of developing and managing cloud-native applications. Furthermore, Microsoft Fabric allows developers to avoid complex infrastructure issues and focus on demanding, project-critical workloads that are manageable, reliable, and scalable. The platform also integrates different technologies, including Power BI, Azure Synapse Analytics, and Azure Data Factory, into one product that users can leverage to optimize data analysis.
Various nationally and globally recognized businesses have adopted Microsoft Fabric to optimize analytics. For instance, Fergusson, a leading North American waterworks, HVAC, and plumbing supplier, uses Fabric to consolidate its analytics (Ulagaratchagan, 2023). The underlying objective is to improve efficiency and reduce delivery time. T-Mobile, one of the biggest providers of wireless communication services, leverages Fabric as part of its transition to optimal data-driven decision-making (Ulagaratchagan, 2023). AON, which offers diverse services to its global customers, uses Fabric to consolidate its technology stack and add more value for its clients (Ulagaratchagan, 2023). These use cases indicate the increasing preference for a unified analytics platform.
Core Workloads in Microsoft Fabric
As illustrated in Figure 1, Microsoft Fabric comprises seven key workloads: Data Factory, Synapse Data Engineering, Synapse Data Science, Synapse Data Warehouse, Synapse Real Time Analytics, Power BI, and Data Activator.
Figure 1: The core workloads of Microsoft Fabric (Source: Ulagaratchagan, 2023)
Data Factory
Data Factory offers modern data integration to help amass, prepare, and transform data from diverse sources, including real-time data, Lakehouse, data warehouses, and databases. The service allows both individual users and developers to transform data with intelligent transformations and exploit a rich cluster of activities (Kromer et al., 2023). Data Factory also delivers fast copy capabilities to data pipelines and dataflows in Microsoft Fabric (Kromer et al., 2023). The rapid data movement enables simple transmission between preferred sources and destinations. For example, users can quickly migrate their data to their data warehouse and layout in Microsoft Fabric for relevant analytics. Based on these properties, the key Data Factory functionalities can be generalized as pipelines and dataflows. The latter facilitates over 300 transformations in the dataflow designer, optimizing flexibility and transformation. Conversely, data pipelines enable out-of-the-box rich data orchestration to accommodate flexible data workflows.
Synapse Data Engineering
Synapse Data Engineering is designed to optimize working with amassed data. For example, users can develop a Lakehouse for all their organizational data (Lucznik, 2023). In such cases, the data engineering service optimally amalgamates the data lake and warehouse to eliminate the friction of ingesting, transforming, and sharing data. Lakehouse is also formulated as a first-class workspace item, improving accessibility. Furthermore, Synapse Data Engineering offers optimal runtime with robust admin controls and high default performance (Lucznik, 2023). The public version of the service features Runtime, which includes Python, Delta, and Spark. The Spark Runtime is also pre-linked to the entire Microsoft Fabric workspace, helping developers get started with the service. Additionally, Microsoft’s Notebook is the main authoring canvas in Synapse Data Engineering, offering developers native Lakehouse integration (Lucznik, 2023). Data engineers can also install R and Python libraries or use Data Wrangler where there is a need for low-code experiences. These attributes translate to great authoring experiences for Spark, collaboration abilities, and instant start capabilities.
Synapse Data Science
Synapse Data Science delivers an end-to-end workflow for developers and data engineers to create complex AI models, collaborate efficiently, and train, implement, and control machine learning (ML) models. These provisions allow Microsoft Fabric to effectively integrate data science with business intelligence (BI) and analytics (Gustafsson, 2023). This advantage can be linked to various Synapse Data Science features and experiences. For instance, the data science service enables data preparation and code generation with Data Wrangler (Gustafsson, 2023). Data Wrangler facilitates easy data preparation and cleansing and allows users to exploit Python’s coding power and reproducibility. Furthermore, Synapse Data Science delivers the Synapse ML Library, the most extensive ML library for Spark (Gustafsson, 2023). The library simplifies scalable and distributed ML, providing access to different AI tools and simple APIs for enriching data and implementing ML models. Moreover, Synapse Data Science accommodates ML model operationalization using the scalable PREDICT function, allowing data engineers to manage predictions without moving data (Gustafsson, 2023). The service also supports R language, optimizing universal usability.
Synapse Data Warehousing
Synapse Data Warehousing provides an amalgamated Lakehouse and data warehouse experience. The service is considered the first transactional typology to support open data formats natively (Sathy, 2023). This quality allows IT teams to collaborate effortlessly and derive actionable insights from amassed data without endangering enterprise governance and security. Synapse Data Warehousing also employs SQL to deliver multi-table ACID transactional guarantees (Sathy, 2023). Specifically, the product is based on the SQL Server Query Optimizer and Distributed Query Processing engine and reinforced to enable full integration, self-optimization, auto-scaling, cross-querying, open data standards, storage and compute separation, and full management. Additionally, Synapse Data Warehousing delivers an intuitive and straightforward experience where new warehouses can be created using only a name and sensitivity label (Sathy, 2023). Data can also be easily loaded in warehouses by drafting T-SQL queries based on the COPY command (Sathy, 2023). These uses and benefits facilitate the effective management and analysis of Big Data.
Synapse Real-Time Analytics
Synapse Real-Time Analytics is integrated into Microsoft Fabric to allow IT teams to manage and analyze large volumes of data from logs, telemetry, Internet of Things (IoT), and other devices. The analytics service employs a query language and engine suited for unstructured, semi-structured, and structured data (Schuster et al., 2023). The real-time analytics is also fully integrated with all Fabric products for seamless data loading, data transformation, and advanced visualization (Schuster et al., 2023). Moreover, the service allows the management of unlimited volumes of data with unlimited concurrent users and queries. A built-in auto-scale that matches available resources with workload variables, including ingestion, CPU usage, memory, and cache, is also included. Unlike conventional products, Synapse Real-Time Analytics allows data engineers to directly run analytical queries on raw data without scripting or building complex data models (Schuster et al., 2023). These applications foster optimal analytical performance and reduce overall costs.
Power BI
Microsoft Fabric incorporates Power BI to allow users to visualize data from different sources, including on-premise data warehouses, cloud-based warehouses, and Excel spreadsheets. Several Power BI-specific features are available for Fabric users. For example, developers and IT teams can access next-generation AI with Copilot (Manis, 2023). Copilot facilitates the integration of large language models into every Power BI layer, optimizing the possible extent of data analysis. Users can leverage this feature for tasks such as producing and editing DAX calculations and creating and customizing reports in seconds. Furthermore, Power BI offers a unified foundation with Direct Lake and OneLake modes, which is crucial for preventing vendor lock-in and minimizing data duplication and management (Manis, 2023). Power BI also facilitates enterprise-grade collaboration with optimal Git integration (Manis, 2023). This feature means users can rapidly and easily connect their workspaces to Azure DevOps repositories to monitor changes, restore previous versions, and manage updates from different team members. These Power BI properties enable end-to-end governance across Fabric.
Data Activator
Microsoft Fabric includes Data Activator to provide a no-code experience for automatic actioning when conditions or patterns are flagged in changing data. The activator service tracks data, Power BI Evenstreams items, and reports to detect when the data matches specific patterns or reaches given thresholds (Iseminger et al., 2024). Once the target conditions are identified, Data Activator takes relevant action, including launching Power Automate workflows or alerting users. These provisions mean that users can formulate a digital nervous system that consolidates and monitors all their data rapidly and at scale. Business users can also conceptualize operating conditions in a no-code experience to start actions, such as Power Automate flows, Teams notifications, and Emails (Iseminger et al., 2024). Furthermore, the users can meet their data needs directly and reduce dependence on internal or external developers and IT teams (Iseminger et al., 2024). Thus, Data Activator fosters optimal business agility and reduces operating costs.
OneLake and Lakehouse Unification in Microsoft Fabric
Perhaps the most significant property of Microsoft Fabric is the unification of OneLake and Lakehouse architecture. Microsoft Fabric Lake (OneLake) is the foundation for all key services. It is integrated into Fabric and offers a common location to hold all organizational data for all Fabric experiences. Furthermore, OneLake is founded on Azure Data Lake Storage (ADLS) Gen2. This basis helps deliver a single Software as a Service (SaaS) product that simplifies Fabric experiences, negating the requirement for users to understand the underlying infrastructure concepts, such as redundancy, Azure Resource Manager, role-based access control (RBAC), and resource groups. OneLake also removes the need for the typical chaotic data silos commonly used in most modern applications. Specifically, the data lake offers one unified storage setup for all developers, optimizing discovery and data sharing and uniformly and centrally enforcing compliance with security requirements and policy.
OneLake employs a hierarchical scheme to simplify management. The data lake is infused with Microsoft Fabric, eliminating the need for up-front provisioning. Furthermore, Fabric offers only one data lake per tenant, translating to a single-pane-of-glass file-system namespace for cloud resources, regions, and users. The contained data is also split into easily manageable containers to optimize handling. Notably, the tenant is at the top of the hierarchical system and maps to OneLake’s root, and individuals can build a virtually limitless number of workspaces within the tenant. The users can simply ingest data into their Lakehouses and commence processing, analysis, and collaboration. All Fabric compute experiences are pre-linked to OneLake, and individual experiences like Data Engineering and Data Warehouse use OneLake as a native store without any additional configuration. OneLake also includes a Shortcut feature to accommodate the rapid mounting of existing platform-as-a-service (PaaS) storage accounts into OneLake. The Shortcut also facilitates easy data sharing between applications and users without duplicating or moving data.
Advantages of Microsoft Fabric
Five main advantages can be inferred from the reported Microsoft Fabric features and experiences:
1. Microsoft Fabric is a holistic and comprehensive analytics platform: Analytics projects typically comprise several subsystems that necessitate obtaining relevant products from multiple vendors. Users often struggle to integrate different analytics products, and the integration can be overly expensive and fragile. Fabric addresses these issues by providing one product with a unified architecture and experiences to suit the requirements of all possible subsystems. Moreover, Fabric delivers the analytics experience as SaaS, allowing the automatic integration and optimization of all services and functions. Users can basically create a Fabric account within seconds and acquire real business value from their data in just a few minutes.
2. Microsoft Fabric is open and lake-centric: Conventional data lakes are typically complicated, and users often struggle to build, integrate, govern, and operate them. The risk of vendor lock-in and data duplication is also high, mainly due to the various data products using dissimilar formats on one data lake. Fabric solves these issues through OneLake. All Fabric workloads are automatically wired to the data lake, and data is arranged optimally within a hub. Furthermore, all collected data is automatically indexed to optimize compliance, management, and data sharing and discovery. Fabric also provides open data formats for all tiers and workloads, meaning users only need to upload data into OneLake once. This feature supports all structured and unstructured data, regardless of format.
3. Microsoft Fabric is AI-powered: Azure OpenAI service is integrated at every Fabric layer, allowing users to maximize data usability. Fabric also infuses Copilot in each experience, facilitating the use of conversational language to structure data pipelines and dataflows, draft codes and functions, develop ML models, and visualize analytics outcomes. The AI services are integrated while maximizing optimal security for users and stored data. For example, Microsoft does not use tenant data to train Copilot’s base language models.
4. Microsoft Fabric is suited for all enterprises regardless of business type and scale: Fabric is fully integrated with all Microsoft 365 applications, ensuring universal applicability. For example, Power BI is deeply infused with popular applications, including PowerPoint, Teams, Excel, and SharePoint, making data from One Lake easily accessible and discoverable from Microsoft 365. Thus, individuals can use Fabric to convert Microsoft 365 applications into products to discover and leverage insights from data. For instance, a Microsoft Excel user can find and analyze data by simply pressing one button.
5. Microsoft Fabric delivers low-cost analytics through unified capacities: Modern analytics projects amalgamate products from different vendors. This scenario provisions computing capacity in various systems, including BI, data warehousing, and data integration. This scheme is wasteful since the capacity of an idle system cannot be used by another system. Conversely, Fabric provides users with one computing pool for all workloads, mitigating the wastage concerns. Users can formulate analytics solutions that freely exploit all workloads without friction between commerce or experiences. The shared computing eliminates idle compute capacities, reducing costs substantially.
Conclusion
Microsoft Fabric is an invaluable product in the modern world, where businesses struggle to manage and interpret Big Data. Fabric provides a unified, end-to-end analytics platform that combines all the tools a company may need. The amalgamation of analytics services reduces the underlying complexities and costs, contributing to the universal accessibility of analytics for all enterprises and individual users. Fabric is also integrated with Microsoft 365, enhancing its general availability. Moreover, AI is incorporated to help customers unlock the full potential of their data. These qualities suggest that Fabric represents the current state-of-the-art in data management and analytics.
References
Sahay, R. (2020). Microsoft Azure architect technologies study companion: Hands-on preparation and practice for exam AZ-300 and AZ-303. Apress.
Manis, K. (2023, May 23). Introducing Microsoft Fabric and Copilot in Microsoft Power BI. Microsoft. https://powerbi.microsoft.com/en-us/blog/introducing-microsoft-fabric-and-copilot-in-microsoft-power-bi/
Sathy, P. (2023, May 23). Introducing Synapse Data Warehouse in Microsoft Fabric. Microsoft. https://blog.fabric.microsoft.com/en-US/blog/introducing-synapse-data-warehouse-in-microsoft-fabric/
Gustafsson, N. (2023, May 23). Introducing Synapse Data Science in Microsoft Fabric. Microsoft. https://blog.fabric.microsoft.com/en-us/blog/introducing-synapse-data-science-in-microsoft-fabric/
Lucznik, J. (2023, May 23). Introducing Synapse Data Engineering in Microsoft Fabric. Microsoft. https://blog.fabric.microsoft.com/en-us/blog/introducing-synapse-data-engineering-in-microsoft-fabric/
Ulagaratchagan, A. (2023, May 23). Introducing Microsoft Fabric: Data analytics for the era of AI. Microsoft. https://azure.microsoft.com/en-us/blog/introducing-microsoft-fabric-data-analytics-for-the-era-of-ai/
Kromer, M. et al. (2023, November 15). What is Data Factory in Microsoft Fabric? Microsoft. https://learn.microsoft.com/en-us/fabric/data-factory/data-factory-overview
Seminger, D. et al. (2023, November 16). What is Data Activator? Microsoft. https://learn.microsoft.com/en-us/fabric/data-activator/data-activator-introduction
Schuster, Y. et al. (2023, December 14). What is Real-Time Analytics in Fabric?Microsoft. https://learn.microsoft.com/en-us/fabric/real-time
