So, you’ve heard about the “fanciest and coolest” role in the data space, called Analytics Engineer, and you are dreaming of becoming one? Have you also heard about Microsoft’s latest and greatest SaaS solution, called Microsoft Fabric, which brings together experiences such as Data Engineering, Data Factory, Data Science, Data Warehouse, Real-Time Analytics, and Power BI onto a shared SaaS foundation? How about getting your skills proofed and certified as a Microsoft Fabric Analytics Engineer?

If you answered yes to all of these questions, then this workshop is for you!

We’ll explain the role of the Analytics Engineer in plain English, without buzzwords and hype. Then, I’ll teach you how to leverage Microsoft Fabric as a unified analytics platform, with its various components, to excel in your Analytics Engineer role and design robust and efficient data solutions for different personas within your organization.

By the time we’re done, you’ll gain a thorough understanding of Microsoft Fabric and its core components, and how it fits into your organizational data workloads. Additionally, since we will cover Microsoft Fabric specifically from the Analytics Engineer’s perspective, you should feel more confident in taking the latest certification exam related to Microsoft Fabric: DP-600 Implementing Analytics Solutions Using Microsoft Fabric.

High-level Agenda Overview
1. Overview of different roles in the data and analytics space
2. What is Microsoft Fabric and why do Analytics Engineers need it
3. Understanding Parquet and Delta file formats
4. Fabric Lakehouse or Fabric Warehouse: which one is (not) for me?
5. Preparing and serving data in Microsoft Fabric
6. Microsoft Fabric for Power BI professionals
7. End to End solution Demo
8. Managing Analytics Development Lifecycle in Fabric
9. Summary and quiz

Detailed Agenda of the day
1. Understanding different roles in the data and analytics space (focus on explaining the Analytics Engineer role and its tasks)
• Difference between Analytics Engineer and Data Engineer/Data Scientist/Data Analyst
• Main Analytics Engineer tasks and responsibilities

2. What is Microsoft Fabric and why do Analytics Engineers need it?
• High-level overview of Microsoft Fabric
• Terminology and user interface
• Microsoft Fabric core components and architecture (storage vs different compute engines)

3. Understanding Parquet and Delta file formats
• Explain details of Parquet/Delta format, as the core storage format options in Fabric.

4. Fabric Lakehouse or Fabric Warehouse: which one is (not) for me?
• Examining use cases for both Lakehouse and Warehouse
• Feature overview/comparison

5. Prepare and serve data in Microsoft Fabric
• Creating objects in a Lakehouse
• Creating objects in a Warehouse
• Understanding file partitioning for better performance
• Moving data between different Microsoft Fabric items (using pipelines, dataflows or notebooks)
• Transforming data by implementing data cleaning techniques
• Designing Star schema in lakehouse and warehouse
• Understanding various data modeling concepts and techniques (I.e. aggregations, denormalization, etc.)
• Performance tuning for Microsoft Fabric workloads
• Querying data with SQL (both Lakehouse and Warehouse)

6. Microsoft Fabric for Power BI professionals (focus on Direct Lake mode)
• Designing and building semantic models (choosing the proper storage mode)
• Optimizing Enterprise-scale semantic models (Performance troubleshooting, leveraging external tools, etc.)
• Exploring and analyzing data (descriptive analytics, data profiling...)

7. End to End solution
a. Demo and potentially lab on building an end-to-end analytic solution using pipelines/notebooks/dataflows for data integration, Lakehouse/Warehouse for storing the data, and Power BI with Direct Lake mode to serve the data

8. Managing Analytics Development Lifecycle in Fabric
• Implementing version control
• Managing PBI Desktop Project (pbip) files
• Understanding deployment solutions (deployment pipelines)
• Using XMLA endpoint for managing and deploying semantic models

9. Summary and quiz
• Provide an interactive way for participants to engage and check what they’ve learned during the workshop