Training, evaluating, and registering an ML model inside Fabric, including how this ties into MLOps and downstream consumption.
A hands-on walkthrough of exploring churn data with notebooks and visualisation in Fabric, aimed at data scientists and analytics engineers.
How to ingest data from external systems into Fabric—covering shortcuts, pipelines, and best practices for staging and governance.
An overview of Microsoft Fabric as a unified analytics platform, and how its DS & AI experience ties together storage, engineering, ML, and BI.
A practical migration journey from batch-heavy Hadoop ecosystems to Spark-first architectures, with tips on performance, cost, and team skills.
Spark isn’t just “another framework” – it’s the backbone of scalable ETL, streaming, and ML. This article explains why every modern data engineer needs Spark fluency.