What you'll learn:
- Power BI with DirectLake. A 170 million row fact table is used to demonstrate we don't need import mode any more!
- Apache Spark Integration into Microsoft Fabric
- Data science in Microsoft Fabric attempting to predict how long a taxi ride in New York City might take
- Ingesting data using the power and flexibility of data pipelines with DataFlows Gen2 and Data Factory
- Building a data warehouse in Microsoft Fabric
- Building a real-time analytics solution using New York City taxi data and Event Streams with KQL
- Semantic Link for Power BI
A deeper dive into what you might ask? A deeper dive than what our initial Microsoft Fabric course covers. We go more deeply into data engineering, data science, data warehousing, Spark, Power BI with DirectLake, Power BI with Semantic Link with end-to-end examples and studies.
If you are a Power BI developer, you will love our deep dive into DirectLake where we use a 170 million row fact table to show that DirectLake is every bit as fast as tabular import mode. Hard to believe but true. We also cover Semantic Link with Power BI allowing Spark notebooks to interact with Power BI datasets.
If data engineering is your specialty we go through and end-to-end project demonstrating how to use Dataflows Gen2 and Data Factory Copy Activity, with pipelines to build an ingestion process.
If your interest is data science we go through an end-to-end machine learning project to demonstrate how Fabric and notebooks can be used or predict how long a taxi ride might take in New York City.
If you have a need to learn more about real-time analytics and KQL, we go through a project where we simulate taxi data being ingested using event streams and KQL.
If you are interested in building a data warehouse, we have an end-to-end data warehouse project showing how this can be done in Microsoft Fabric.
Interested in Apache Spark? We cover the important areas of Spark and how it integrates into Microsoft Fabric.
By the time you complete this 10+ hour course you will feel completely comfortable using all the most commonly used experiences in Microsoft Fabric.
