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Implementation of PySpark Merge in Fabric
Learn how to implement PySpark merge statements in Microsoft Fabric with Manuel Quintana. This hands-on session walks you through writing, testing, and executing code directly in a Lakehouse environment.
Dive into the power of PySpark in Microsoft Fabric with Implementation of PySpark Merge in Fabric, led by Manuel Quintana. This course introduces you to the concept of using PySpark to merge data in Lakehouses, helping you understand how Fabric handles large-scale data processing with flexibility and performance in mind.
Manuel walks you through a hands-on example of writing a PySpark merge statement in Fabric, showing you where to write, how to test, and how to run the code directly in the platform. Whether you're new to PySpark or exploring how Fabric supports advanced data workflows, this session provides a practical, focused introduction to a powerful technique.
Course Outline ( Free Preview)
Module 00 - What You Need to Get Started
Download any necessary files and ensure your Microsoft Fabric environment is ready. This quick setup module helps you follow along smoothly.
Module 01 - Introduction
Meet Manuel Quintana and get an overview of what you'll learn in this focused session on using PySpark merge within Microsoft Fabric.
Module 02 - Why Use PySpark Merge
Understand the benefits of using PySpark merge for managing data in Lakehouses. Learn when and why to apply this powerful technique.
Module 03 - Inserting Records with PySpark Merge33 min.
Learn how to use PySpark merge to insert new records into your Fabric Lakehouse environment with clean, efficient code.
Module 04 - Updating Records with PySpark Merge11 min.
Explore how to update existing data using merge logic. This module shows how PySpark handles dynamic record updates based on conditions.
Module 05 - Deleting Records with PySpark Merge10 min.
See how to remove records using PySpark merge operations. Manuel walks through common scenarios and best practices.
Module 06 - Optional Conditions17 min.
Take your merge logic further by adding optional conditions. This module helps you refine control over when and how records are inserted, updated, or deleted.
Module 07 - Using Parameters14 min.
Learn how to make your PySpark merge scripts more flexible and reusable by incorporating parameters into your logic.
Manuel Quintana is a Senior Consultant, Trainer, and Content Author at Pragmatic Works with more than 12 years of experience helping organizations modernize their data and analytics platforms. He specializes in Microsoft Fabric and works extensively across its end-to-end workloads, including data engineering, data warehousing, data integration, real-time analytics, and business intelligence. Manuel helps organizations design and implement scalable data solutions that transform raw data into actionable insights while enabling self-service analytics and AI-driven decision making.
In addition to his consulting work, Manuel has spent over a decade developing and delivering technical training for thousands of professionals worldwide. His expertise spans the Microsoft data platform, including T-SQL, SQL Server Integration Services (SSIS), SQL Server Analysis Services (SSAS), SQL Server Reporting Services (SSRS), Power BI, Power Apps, Power Automate, Azure data services, and Microsoft Fabric. Known for his practical, hands-on teaching style, Manuel specializes in helping teams adopt modern analytics architectures and get measurable business value from their Microsoft investments.
Manuel is also an accomplished author, having written books and training content focused on Power BI and the Microsoft Power Platform. Beyond client engagements, he actively contributes to the Microsoft data community through conference presentations, user groups, webinars, and technical events, where he frequently shares expertise on Microsoft Fabric, Power BI, modern data architecture, and analytics best practices. When he's not consulting, teaching, or creating content, Manuel enjoys spending time with his wife, Lindsey, and their two children, Parker and Sebastian.
