Assessing Column Data looks at the tools and features in Power BI Desktop and Power Query Editor for assessing and profiling data within a dataset's columns. We cover essential characteristics such as column data types through to distribution and statistical properties of a column's data. This course shows you how to use the various graphical tools in Power Query Editor to visualize data distribution, including uniqueness and distinct values. These tools provide access to filtering functions and detection of empty column values and potential errors due to data type mismatches.
This course also looks at factors that impact correctly importing table relationships into a Power BI data model and pitfalls to look for when assessing and profiling column data.
- Learn how to carry out column profiling and assess table relationships in Power BI and Power Query Editor
This course is intended for anyone who wants to use Power BI for data analysis and wants to learn how to assess the characteristics of their data before doing so.
To get the most out of this course, you should have some experience with Power BI.
Hi and welcome to this Power BI course on Assessing Data Characteristics. The theme of this course is to be forewarned is to be forearmed. This is a well-known saying or proverb, but what does it mean in the context of data and Power BI? Well, before you can create dashboards and reports, you need to import or ingest data into a Power BI model. Not all data sources are created equal. Databases enforce data types, while text files, spreadsheets, and some data streams either don't typed data, or their data typing could be described as loose.
Understanding your data characteristics in terms of data types, relationships between tables, files, and columns will assist in the import process and model design. Power BI and Power Query have easy-to-use features for assessing the size, range, and distribution of data within a column. Profiling a column's data will alert you to issues such as duplicates, inconsistent values, and potential data type errors. Being aware of these problems will allow you to address them in the model design or when importing the data. Alternately you may want to clean the data source before importing it into Power BI. In either case, Power BI column profiling tools will save you significant time over manually investigating the data source yourself.
This course's content isn't overly complex and doesn't rely on any specialist knowledge, but I'm going to assume that you've already used Power BI in some minimal way, at least. While it isn't essential, I also assume you've completed the Getting Data into Power BI course, as I won't be going into detail regarding connecting to data sources.
My name is Hallam Webber, and I'll be your instructor for this course; we welcome all comments and feedback, so please feel free to reach out and get in touch with us at firstname.lastname@example.org with any feedback, positive or negative.
Hallam is a software architect with over 20 years experience across a wide range of industries. He began his software career as a Delphi/Interbase disciple but changed his allegiance to Microsoft with its deep and broad ecosystem. While Hallam has designed and crafted custom software utilizing web, mobile and desktop technologies, good quality reliable data is the key to a successful solution. The challenge of quickly turning data into useful information for digestion by humans and machines has led Hallam to specialize in database design and process automation. Showing customers how leverage new technology to change and improve their business processes is one of the key drivers keeping Hallam coming back to the keyboard.