Common use cases for building solutions with Microsoft Fabric User data functions (UDFs)
Ready to dive into the world of Microsoft Fabric and see how you can build some amazing solutions? Let’s talk about User data functions (UDFs) in Fabric and how they can make your data processes and pipelines super-efficient.
Why use Fabric UDFs?
We all know data engineering can be a bit tricky, especially when it comes to data quality and complex analytics. That’s where UDFs come in handy. They let you add custom logic to your data workflows, making everything run smoother and more consistently. Think of Fabric UDFs as your secret weapon for tackling those tough data challenges. Whether you’re transforming data, running advanced analytics, or meeting unique business needs, UDFs give you the flexibility and control to build robust and scalable applications.
Let’s explore some common use cases in the real world where Fabric UDFs really shine in this blog post.
Analytics and Data processing
The primary use case is to transform, validate and clean your data using a Medallion architecture into a SQL Database, Lakehouse and Warehouse in Fabric. You can build analytical and data driven applications once your data in gold layer. API for GraphQL is your data access layer to connect to your data in Fabric. This allows an organization to store, process, and analyze healthcare data from multiple sources, providing valuable insights for improving patient care and operational efficiency.
Here are some examples:
- Medallion architecture: The organization implements a Medallion architecture to organize data into different layers: Bronze, Silver, and Gold. The Bronze layer contains raw data ingested from various sources, such as electronic health records (EHRs), medical devices, and insurance claims. The Silver layer processes and cleans the data, removing duplicates and correcting errors. The Gold layer contains aggregated and enriched data, ready for analysis and reporting.
- Data pipelines: The organization uses data pipelines to automate the ingestion and processing of healthcare data. For instance, a pipeline is set up to ingest EHR data from multiple hospitals into the bronze layer. Another pipeline processes this data, transforming it into a standardized format and storing it in the silver layer. Finally, a pipeline aggregates the data and stores it in the gold layer for analysis.
- ELT/ETL Tasks: The organization employs ELT (Extract, Load, Transform) and ETL (Extract, Transform, Load) tasks to manage data processing workflows. ELT tasks are used to quickly load large volumes of raw data into the Bronze layer, where it is transformed and cleaned in the Silver layer. ETL tasks are used for more complex transformations, such as combining data from different sources and enriching it with external information, before loading it into the Gold layer.
Integration with external services
The primary use case is to get data into Fabric from external services and APIs using custom code. This allows the organization to aggregate and process data from multiple sources, providing a comprehensive view of their financial operations.
Here are few examples:
- ISV solutions: An organization partners with an Independent Software Vendor (ISV) that provides a financial data aggregation service. Using custom code, the organization integrates this service into Fabric, allowing them to pull real-time financial data from various banks and financial institutions. This data is then used to provide customers with up-to-date account balances, transaction histories, and financial insights.
- Data processing/transformation architectures: An organization uses custom code to connect to external APIs that provide market data, such as stock prices and economic indicators. This data is ingested into Fabric, where it undergoes processing and transformation. The organization applies various algorithms to clean, normalize, and enrich the data, making it suitable for analysis and reporting. This processed data is then used to generate financial reports and dashboards for internal stakeholders.
- Data enrichment: An organization enriches its internal customer data by integrating external data sources using custom code. For instance, they connect to a third-party API that provides demographic information based on customer addresses. This enriched data is then used to create more accurate customer profiles, enabling personalized marketing campaigns and targeted financial products.
Build personalized customer experiences
Using Fabric, the organization can analyze customer data to create personalized product recommendations. For example, when a customer logs into the website, the application can display products that align with their preferences and past purchases. This personalized approach enhances the shopping experience, making it more relevant and engaging for the customer.
Here are a few examples of personalized experiences:
- Recommendation engines: Fabric’s powerful data processing capabilities enable the organization to build sophisticated recommendation engines. By analyzing patterns in customer behavior and purchase history, the recommendation engine can suggest products that are likely to interest the customer. This not only increases sales but also helps customers discover new products they might not have considered
- Shipping: In the shipping domain, Fabric can optimize delivery routes and schedules. By analyzing data such as order volume, delivery locations, and traffic conditions, the organization can plan the most efficient routes for its delivery trucks. This ensures timely deliveries and reduces operational costs. Additionally, customers can receive updates on their order status, enhancing their overall experience.
Conclusion
Microsoft Fabric offers a powerful and flexible platform for managing and analyzing user data with ease. Fabric User data functions can help you automate data processing tasks, integrate with external services, and perform advanced analytics, all within a scalable and cost-effective environment. Get started with free trial today and unlock the full potential of your data with Microsoft Fabric User data functions. Submit your feedback on Fabric Ideas and join the conversation on the Fabric Community.
