Transform and Analyze Data with Microsoft Fabric

Unlock the power of Microsoft Fabric for data transformation and analysis. This hands-on course teaches you how to manipulate, explore, and optimize your data, whether you’re working with SQL, Python, or low-code tools. Develop the skills to clean, filter, and merge data while optimizing performance for fast and efficient analysis.

Key Learning Outcomes

1. Master Data Engineering with Fabric: Understand how to structure your data effectively. Learn about common data models, including star and snowflake schemas, and how to select the best approach for your specific data needs.
2. Transform, Explore, and Analyze Data: Discover key techniques for data transformation and exploration, such as cleansing, filtering, aggregating, and merging data. Practice these tasks using SQL and Python in the Fabric environment.
3. Optimize Performance: Improve the speed and efficiency of your Fabric workflows. Use tools like the Fabric Capacity Metrics app and Delta Lake optimization techniques to monitor and enhance performance across your Fabric account.

Who Should Take This Course?

This course is ideal for data professionals, analysts, and engineers looking to master data manipulation and optimization in Microsoft Fabric. It is beginner-friendly but assumes a basic understanding of data workflows.

Certification Preparation

Get closer to earning Microsoft’s Fabric Analytics Engineer Associate certification (DP-600). This course covers essential topics and skills required for the exam, including data transformation, modeling, and performance optimization.

Syllabus

Data Engineering with Microsoft Fabric

Get an introductory overview of the basic elements of data engineering in Microsoft Fabric, from key solution design items like the star schema to the various features in Fabric supporting data exploration and transformation.

Implement a Star Schema for a Lakehouse or Warehouse

Drill down into specific aspects to consider when implementing star schemas in Microsoft Fabric, including creating fact and dimension tables, managing slowly changing dimensions and other special dimension scenarios, and normalizing and de-normalizing data.

Transform, Explore, and Analyze Data

Drill down into key areas of data transformation and exploration: cleansing, filtering, aggregating and merging data.

Optimize Performance

Learn about tools available to monitor the performance of a Fabric solution and how to implement performance improvements in the Fabric infrastructure and individual items, including optimization of delta lake tables.