This is a short series on the Modern Data Platform in Microsoft Fabric. The articles in this series are:
- Create Raw Zone Tables using Generative AI - In this first article on the Fabric Modern Data Platform, we look at how to use Generative AI to build tables.
- Metadata Driven Pipelines (Full Load) - Learn how you can create a full data load process in Fabric.
- Configuring the On-Premises Data Gateway - Learn how to install and configure the on-premises data gateway for a hybrid design.
- Using Python notebooks to save money in Fabric This article compares the cost of Spark and Python notebooks
- Faster Data Engineering with Python Notebooks - Learn about the Python Polars library.
- Leveraging DuckDB for OLAP Workloads - This article examines how DuckDB can be used to execute analytic queries
- Two New Shortcuts for Fabric Lakehouse Developers - This article shows the new shortcuts for accessing remote data.
- Remotely Engineer Fabric Lakehouse objects - Learn how to use local tools on your workstation to work with Fabric.
- Metadata Driven Pipelines (Incremental Load) - See how an incremental load pattern can be used to only add changed data.
- Going Native with Fabric Spark Pools - This article examines how you can use Spark pools to run your notebooks
- Seeding a Fabric Warehouse with dbt for Visual Studio Code - Learn about using dbt to get data into your warehouse.
- Data Modeling with dbt for Visual Code: The Fabric Modern Data Platform - This article continues with dbt, this time as a way to help you manage your data model.