As the Kansas City Chiefs and the Philadelphia Eagles prepare to face off once again in the Super Bowl, it's the perfect opportunity to explore the capabilities of Microsoft Fabric in analyzing NFL data. Keep reading to learn about the many components within Fabric’s cohesive platform and see how you can leverage them to gain insight into your data.
OneLake is Your One Place for Data
One of the key selling points of Fabric is the integration of your entire data estate into a single platform. OneLake, built into Fabric, provides a unified, SaaS-based storage solution that simplifies data management and eliminates silos for all developers without requiring Azure account knowledge. OneLake Explorer simplifies the process of navigating your data estate, from raw data files to structured data warehouse tables and seamlessly integrates with File Explorer. This tool allows end users to easily manage and explore their data.
Low-Code Data Transformation
Fabric offers multiple ways to ingest data including Data Factory pipelines, Dataflow Gen 2, or simply uploading files from your local machine. Dataflow Gen 2 offers a low-code user interface to ingest, transform, and publish results back into your Lakehouse destination. Here, I’ve leveraged a dataflow to cleanse, add new columns, and merge data sources, to better capture attributes about NFL teams and filter that table to the current records ignoring the history of relocations and renaming some franchises have undergone.
Ad Hoc Analysis with Notebooks
After uploading the data, I leveraged the data science component in Fabric to write PySpark queries for ad hoc analysis. Philadelphia Eagles star running back, Saquon Barkley, has been dominant this season rushing for over 2,000 yards. Using PySpark, I captured the average yards per rush attempt by the Eagles based upon the rush direction. The Eagles’ running game has been notably better behind right tackle Lane Johnson.
Building a Data Warehouse with T-SQL
Fabric Data Warehouse offers a next-generation, lake-centric solution that integrates seamlessly with Power BI, supports open data formats, and offers industry-leading performance, scalability, and ease of use, enabling efficient data management and analysis without compromising security or governance. Using T-SQL language, I’m able to add structure to the data, building out a data warehouse with a schema, staging tables, stored procedures, tables, and views.
For those who are more comfortable with SQL Server Management Studio (SSMS), you can leverage the SQL connection string to query and manage the warehouse outside of the Fabric portal, as well.
Configuring Semantic Models
In Microsoft Fabric, Power BI semantic models are a logical description of an analytical domain, with metrics, business friendly terminology, and representation, to enable deeper analysis. You can easily configure semantic models to establish relationships across tables, enabling analysts to conduct reporting.
Visualizing Data in Power BI
Finally, visualize your data in Power BI and share your insights with others. For instance, you can highlight the historical matchups between the Eagles and Chiefs, including their last Super Bowl face off in 2022, where the Chiefs edged out the Eagles by just 3 points.
Fabric also features robust governance and security capabilities complemented by Microsoft Purview. Plus, the Real-time intelligence hub empowers everyone in your organization to extract insights and visualize their data in motion. Not to mention, Microsoft Copilot and other generative AI features are embedded throughout the Fabric to work more efficiently.
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