Learn Microsoft Fabric Notebooks in 2026 - Full Course!

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Welcome to the ultimate Microsoft Fabric Notebook Data Engineering Masterclass! Join Aleksi, an experienced solution architect and data engineering leader, as he guides you through mastering notebooks in Microsoft Fabric for data engineering. Whether you're a beginner or experienced professional, this masterclass offers 15 detailed lessons to help you elevate your data engineering skills.

What You’ll Learn:
🔷 Overview of notebooks as a data engineering tool in Microsoft Fabric
🔷 How to use NotebookUtils for managing secrets and running other notebooks
🔷 Processing CSV and JSON files with PySpark notebooks
🔷 Using parameters, exit values, and metadata-driven logic in notebooks
🔷 Leveraging Spark SQL for data transformation and exploration
🔷 Fetching and parallelizing API calls for efficient data retrieval
🔷 Working with T-SQL and vanilla Python notebooks in Microsoft Fabric
🔷 Techniques for handling schema drift and processing Excel files
... and much more!

Key Takeaways:
✨ Master the fundamentals and advanced techniques of Fabric notebooks
✨ Learn to process semi-structured data like JSON and CSV files
✨ Develop metadata-driven notebooks for faster, configuration-based development
✨ Explore real-world scenarios for API integration and schema handling

Related Hashtags:
#MicrosoftDataPlatform #DataFactory #RealTimeData #BigData #DataPipelines #DataEngineeringTutorial #DataScience #DataProcessing #SparkSQL #ETL #DataTransformation #Lakehouse #DataWarehouse #FabricNotebooks #CloudDataEngineering #DataStreaming #JSONHandling #ExcelProcessing #APICalls #DataSchema #AnalyticsEngineering #DataIntegration #PythonForDataEngineering #AzureTraining #TechTutorials #digitaltransformation

🏁 Timestamps:
00:00 Intro
04:31 Overview
25:03 NotebookUtils
41:38 Processing CSV files
48:48 Parameters and exit values
54:23 SparkSQL
1:00:37 Explode function
1:06:44 Processing JSON files
1:18:55 Running a notebook from another notebook
1:29:56 Fetching data from an API
1:44:57 Parallel API calls
1:57:28 T-SQL notebooks
2:08:36 Processing Excel files
2:17:38 Vanilla python notebooks
2:31:55 Metadata-driven notebooks
2:45:14 Handling schema drift