Momentum One Academy · lab
Use Delta Tables in Apache Spark
This lab introduces Delta tables through a Fabric Lakehouse workflow. Participants work with a products dataset and observe how a managed table can be queried, updated and reviewed over time, then extend the same environment to a small streaming scenario.
What the lab covers
- Reading product data into a managed Delta table with Spark.
- Updating selected values and examining the table transaction history.
- Comparing an earlier table version with the current state.
- Summarizing product data with Spark SQL and PySpark.
- Writing simulated IoT events from a structured stream to a Delta destination.
Learning outcome
Participants can explain how Delta transaction history supports traceability and version-based analysis. They also see how batch table operations and streaming ingestion can share Lakehouse storage while serving different processing needs.
Workshop connection
This overview supports Module 05 — Spark SQL. It connects SQL analysis to Delta table behavior and gives the team a concrete example for discussing history, updates and streaming data in a Lakehouse context.
Access note
The lab requires access to a Microsoft Fabric capacity, either paid or trial. Full participant instructions and the training dataset are supplied during the workshop. This page is an overview only and does not publish the PDF, source code or sample assets.
Further learning
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