Description

Introduction
This training dives deep into the Power BI M language, equipping participants with advanced skills to manipulate, transform, and shape data efficiently. The course covers complex data transformation scenarios, custom functions, and performance optimization techniques, enabling participants to build scalable and reusable ETL solutions within Power BI.

Prerequisites

1. Basic knowledge of Power BI Desktop and Power Query
2. Familiarity with ETL concepts (Extract, Transform, Load)
3. Basic understanding of data types and relational data

Table of Contents

1. Overview of M Language
1.1 What is M and its role in Power Query
1.2 M vs DAX: Understanding the difference
1.3 Exploring the Power Query Advanced Editor

2. M Language Fundamentals
2.1 Syntax, keywords, and data types
2.2 Variables, let expressions, and query structure
2.3 Basic functions and operators

3. Data Shaping with M
3.1 Transforming rows and columns programmatically
3.2 Filtering, sorting, and grouping using M
3.3 Pivot, unpivot, and table transformations

4. Advanced M Functions
4.1 Text, numeric, date, and list functions
4.2 Record and table functions for complex transformations
4.3 Creating and using custom functions

5. Query Combining Techniques
5.1 Merging and appending queries programmatically
5.2 Dynamic query referencing
5.3 Handling multiple data sources efficiently

6. Parameterization and Automation
6.1 Using parameters in queries for dynamic data shaping
6.2 Conditional logic and branching with M
6.3 Automating repetitive transformations

7. Performance Optimization
7.1 Best practices for query folding
7.2 Minimizing refresh time with efficient M code
7.3 Debugging and error handling in M

8. Real-World Scenarios and Hands-On Labs
8.1 Transforming large datasets for reporting
8.2 Creating reusable M scripts for multiple reports
8.3 Integrating M transformations into Power BI dashboards

Participants will leave this training with advanced M language skills, capable of shaping and transforming data in Power BI at scale. They will be able to build reusable, efficient, and automated ETL pipelines that enhance reporting and analytics capabilities.