Power Query: Advanced Data Integration with Power BI.
Power Query is the Microsoft Data Connectivity and Data Preparation technology that enables business users to seamlessly access data stored in hundreds of data sources and reshape it to fit their needs, with an easy to use, engaging and no-code user experience.
Power Query acts or can be considered as an ETL tool which means that it extracts data from almost any data source, transforms that data somehow and then loads it somewhere. What makes Power Query such a ground-breaking tool is it is the first tool built specifically for business users but there’s more under the hood.
Course Delivery Options
The course will take place in a classroom with no more than 15 students in order to maintain a good level of interactivity.
Before attending this course, it is recommended that students have the following skills:
- Desire to learn more about Power Query
- Prior exposure to Power Query and Excel is recommended.
- Understanding of ETL processes is recommended.
- Knowledge about any functional programming language is recommended, but not necessary.
After this Course
Upon completion of this course, the student will be able to:
- Understand the key capabilities and characteristics of Power Query
- Understand the different elements that can be created with Power Query
- Understand the query folding process done by Power Query
- Create their own functions
- Debug and review M code
- Optimize Power Query transformations and processes
- Profile and isolate data errors
Paco, a Microsoft Data Platform MVP and SolidQ North America Managing Director, is the PASS Big Data Virtual Chapter Leader, SQL Saturday Barcelona Organizer and assistant at the PASS Atlanta BI User Group. He is finishing his PhD thesis about “Analyzing Social Data with Machine Learning.” He is an MCT and MCP on BizTalk Server and SQL Server. He is coauthor of Microsoft Training Kit 441.
Paco is a frequent speaker at large and small conferences. SQL Saturdays, TechEd’s, PASS BA, PASS Summit, DevWeek London, and PAW Chicago and London.Paco, a Microsoft Data Platform MVP and SolidQ North America Managing Director, is the PASS Big Data Virtual Chapter Leader, SQL Saturday Barcelona Organizer and assistant at the PASS Atlanta BI User Group. He is finishing his PhD thesis about “Analyzing Social Data with Machine Learning.” He is an MCT and MCP on BizTalk Server and SQL Server. He is coauthor of Microsoft Training Kit 441.
Paco is a frequent speaker at large and small conferences. SQL Saturdays, TechEd’s, PASS BA, PASS Summit, DevWeek London, and PAW Chicago and London.
Víctor M. García Sánchez
Besides, trying to share knowledge to community through SolidQ and personal blog, collaborating with TechNet performing webcast and speaking in events.
Rushabh leads software development, application development, product management, and customer enablement. With an entrepreneurial spirit and passion for finding new ways to solve complex business challenges, he has deep empathy for customers and end users which guides his advocacy and influence over product direction. Rushabh strives to create an environment of high energy and creativity. He has been providing Business Intelligence, SQL Server and Information Services architecture solutions, training and mentoring services to enterprise customers since 1998.
Brett is Principal Architect, Managing Consultant and Mentor for multiple high visibility projects delivering a holistic view of critical business information and processes covering a variety of vertical markets.
Brett is also Chapter Leader for the Triangle SQL Server User Group (TriPASS) and a published author of numerous articles and books
Module 1. What is Power Query?
- What is Power Query?
- What can you do with Power Query?
- Data Types
Module 2. Get data
- Data Source
- Getting data
Module 3. Transforming data
- Query Editor
- Data cleansing and transformations
- Working with columns
Module 4. Advanced Transformations
- Advanced Transformations
- Query Dependency
- Data Profiling
Module 5. R and Python with Power Query
- R Language
- Performance tips
Module 6. M internals and optimization
- M Language
- Query Folding
- Data Privacy
Module 7. Dataflows
Module 8. Advanced Dataflows
- Common Data Model
- Enhanced Compute Engine
- Shared Datasets
- Additional Services
- Best Practices
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