In this session, we’ll take a practical deep dive into Spark-based data engineering in Microsoft Fabric. We’ll walk through proven techniques and repeatable patterns for solving common data engineering challenges, covering advanced Spark SQL functions (including explode, concat_ws, and related transformation patterns), effective approaches to semi-structured data, and strategies for building reliable, maintainable pipelines.

We’ll also highlight AI-related capabilities available in Azure Spark, including built-in and integrated functions that support text processing, enrichment, classification, and feature engineering directly within Spark workloads, along with guidance on where these functions fit (and where they don’t) in real-world pipelines.

Audience: The presentation is technical and aimed at data engineers and analytics engineers using Spark in Fabric. The session is beginner-friendly and case-based, with roughly 50% focused on advanced techniques.

(Preferred dates: April–May 2026)