Microsoft Fabric: A SaaS Analytics Platform for the Era of AI

Microsoft Fabric

Microsoft Fabric is a new and unified analytics platform in the cloud that integrates various data and analytics services, such as Azure Data Factory, Azure Synapse Analytics, and Power BI, into a single product that covers everything from data movement to data science, real-time analytics, and business intelligence. Microsoft Fabric is built upon the well-known Power BI platform, which provides industry-leading visualization and AI-driven analytics that enable business analysts and users to gain insights from data.

Basic concepts

On May 23rd 2023, Microsoft announced a new product called Microsoft Fabric at the Microsoft Build conference. Microsoft Fabric is a SaaS Analytics Platform that covers end-to-end business requirements. As mentioned earlier, it is built upon the Power BI platform and extends the capabilities of Azure Synapse Analytics to all analytics workloads. This means that Microfot Fabric is an enterprise-grade analytics platform. But wait, let’s see what the SaaS Analytics Platform means.

What is an analytics platform?

An analytics platform is a comprehensive software solution designed to facilitate data analysis to enable organisations to derive meaningful insights from their data. It typically combines various tools, technologies, and frameworks to streamline the entire analytics lifecycle, from data ingestion and processing to visualisation and reporting. Here are some key characteristics you would expect to find in an analytics platform:

  1. Data Integration: The platform should support integrating data from multiple sources, such as databases, data warehouses, APIs, and streaming platforms. It should provide capabilities for data ingestion, extraction, transformation, and loading (ETL) to ensure a smooth flow of data into the analytics ecosystem.
  2. Data Storage and Management: An analytics platform needs to have a robust and scalable data storage infrastructure. This could include data lakes, data warehouses, or a combination of both. It should also support data governance practices, including data quality management, metadata management, and data security.
  3. Data Processing and Transformation: The platform should offer tools and frameworks for processing and transforming raw data into a usable format. This may involve data cleaning, denormalisation, enrichment, aggregation, or advanced analytics on large data volumes, including streaming IOT (Internet of Things) data. Handling large volumes of data efficiently is crucial for performance and scalability.
  4. Analytics and Visualisation: A core aspect of an analytics platform is its ability to perform advanced analytics on the data. This includes providing a wide range of analytical capabilities, such as descriptive, diagnostic, predictive, and prescriptive analytics with ML (Machine Learning) and AI (Artificial Intelligence) algorithms. Additionally, the platform should offer interactive visualisation tools to present insights in a clear and intuitive manner, enabling users to explore data and generate reports easily.
  5. Scalability and Performance: Analytics platforms need to be scalable to handle increasing volumes of data and user demands. They should have the ability to scale horizontally or vertically. High-performance processing engines and optimised algorithms are essential to ensure efficient data processing and analysis.
  6. Collaboration and Sharing: An analytics platform should facilitate collaboration among data analysts, data scientists, and business users. It should provide features for sharing data assets, analytics models, and insights across teams. Collaboration features may include data annotations, commenting, sharing dashboards, and collaborative workflows.
  7. Data Security and Governance: As data privacy and compliance become increasingly important, an analytics platform must have robust security measures in place. This includes access controls, encryption, auditing, and compliance with relevant regulations such as GDPR or HIPAA. Data governance features, such as data lineage, data cataloging, and policy enforcement, are also crucial for maintaining data integrity and compliance.
  8. Flexibility and Extensibility: An ideal analytics platform should be flexible and extensible to accommodate evolving business needs and technological advancements. It should support integration with third-party tools, frameworks, and libraries to leverage additional functionality.
  9. Ease of Use: Usability plays a significant role in an analytics platform’s adoption and effectiveness. It should have an intuitive user interface and provide user-friendly tools for data exploration, analysis, and visualisation. Self-service capabilities empower business users to access and analyse data without heavy reliance on IT or data specialists.
    These characteristics collectively enable organisations to harness the power of data and make data-driven decisions. An effective analytics platform helps unlock insights, identify patterns, discover trends, and drive innovation across various domains and industries.

What is SaaS, and how is it different from PaaS?

SaaS stands for Software as a Service, which means that customers can access and use software applications over the Internet without having to install, manage, or maintain them on their own infrastructure. SaaS applications are hosted and managed by the service provider, who also takes care of updates, security, scalability, and performance. Customers only pay for what they use and can easily scale up or down as needed.
PaaS stands for Platform as a Service, meaning customers can use a cloud-based platform to develop, run, and manage their own applications without worrying about the underlying infrastructure. PaaS platforms provide tools and services for developers to build, test, deploy, and manage applications. While customers have more control and flexibility over their applications, at the same time, they are more responsible for maintaining them.

How do these concepts apply to Microsoft Fabric?

With the preceding definitions, we see that Microsoft Fabric is a great fit to be called a SaaS Analytics Platform. Depending on our role, we can now use various items to integrate the data from multiple systems, store data in unified cloud storage, and process and transform the data in a scalable and performant way. On top of that, we can run advanced AI and ML techniques to gain the most out of the platform. As Microsoft Fabric is built upon the Power BI platform, ease of use, strong collaboration and wide integration capabilities are also on the menu. All these points mean that customers do not have to deal with the complexity of integrating and managing multiple data and analytics services from different vendors. They also do not need to deal with cumbersome configuration and maintenance loads, thanks to the SaaS characteristic of the platform. Customers can now use a single product with a unified experience and architecture that provides all the capabilities they need for data integration, data engineering, data warehousing, data science, real-time analytics, and business intelligence.

The benefits of Microsoft Fabric

Microsoft Fabric offers several benefits for customers who want to unlock the potential of their data and put the foundation for the era of AI. Some of these benefits are:

  • Simplicity: We can sign up within seconds and get real business value within minutes. We do not have to worry about provisioning, configuring, or updating infrastructure or services. We can use a single portal to access all the features and functionalities of Microsoft Fabric.
  • Completeness: We can use Microsoft Fabric to address every aspect of our analytics needs end-to-end. We can ingest data from various sources, integrate it, model it, visualise it, analyse it, and run AI and ML models on it to gain data-driven insights that lead to fact-based decision-making and scientific predictions that can help businesses invest more confidently.
  • Collaboration: We can use Microsoft Fabric to empower every team in the analytics process with the role-specific experiences they need. Data engineers, data warehousing professionals, data scientists, data analysts, and business users can work together seamlessly on the same platform and share data, insights, and best practices.
  • Governance: With Microsoft Fabric, we can create a single source of truth that everyone can trust. We can use unified governance features to manage data quality, security, privacy, compliance, and access across the entire platform.
  • Innovation: We can use Microsoft Fabric to leverage the latest technologies and innovations from Microsoft and its partners. We can benefit from generative AI and language model services such as Copilot to create everyday AI experiences that transform how users and developers spend their time. With OneLake being the central data lake, we can now support open formats such as Parquet and integrate with other cloud platforms such as Amazon S3 and Google Cloud Storage.

Microsoft Fabric is a game-changer for organisations that want to transform their businesses with data and analytics. It is a SaaS Analytics Platform that covers end-to-end business requirements from a data and analytics point of view. It is built upon the well-known Power BI platform and extends the capabilities of Azure Synapse Analytics to all analytics workloads. It is simple, complete, collaborative, governed, and innovative. It is Microsoft Fabric.

Microsoft Fabric usage is persona-based

Microsoft Fabric enables organisations to empower various users to utilise their experience in the analytics platform. So, based on our persona:

  • Data engineers can use Data Engineering tools and features to transform large-scale data. For example, we can use Spark notebooks to clean and enrich data from various sources and store it in Parquet format in the OneLake.
  • Data integration developers can use the Data Factofry capabilities in Microsoft Fabric to create integration pipelines with either Dataflows Gen2 or Data Factory Pipelines to collect data from hundreds of different data sources and land it into OneLake.
  • Data scientists can use the Data Science tools and features to build and deploy ML models using familiar tools like Python and R.
  • Data warehouse professionals can use the Data Warehouse tools and features to create enterprise-grade relational databases using SQL. For instance, we can use Synapse Data Warehouse to create tables and views that join data from different sources and enable fast querying.
  • As business analysts, we can use Power BI in Fabric to gain insights from data and share them with others. We can do everything we used to do in Power BI; for instance, we can use Power BI Desktop to create interactive reports and dashboards that visualize data from various sources and publish them to Power BI Service. We can also create story-telling reports and dashboards on top of the already created datasets in Fabric.
  • We can use the Real-Time Analytics capabilities to ingest and analyse streaming data from IoT devices or logs and query streaming data using Kusto Query Language (KQL).
    Here is the thing, all of the sophisticated tools and features are transparent to the end-users. They still access their beloved Power BI reports and dashboards as usual, but they just seamlessly get more with Fabric. They will hear less about technology limitations and have a better experience with well-performing and faster reports and dashboards.


Fabric is an exciting product that promises to simplify and enhance the analytics experience for users. Just be aware of the fact that it is currently in preview and, consequently, is subject to change. To learn more about Fabric, visit

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