Правила очистки и трансформации исходных данных в Power BI. Таблица фактов. Курс Power BI.

In this lesson, we'll discuss the rules for cleansing and transforming source data in Power BI, using a fact table as an example. This is part of our lesson on Financial Analysis and Modeling in Power BI. In the full lesson, we also cover the theory behind data cleansing and transformation rules, demonstrate additional examples—an account dictionary, a calendar, and process pivot data. In our courses, we break each lesson into a separate video, making it easier to digest the information and quickly return to the material you need. And a little theory. Basic rules: Keep only the columns that are essential for your work. Not all system fields included in the standard data export are necessary. A column should contain only one data type (for example, the Date column should not contain text values ​​such as "none," "not installed," etc.) Set the required data format and eliminate unnecessary details. For example, leave only the date if the transaction time is not needed for subsequent analysis. Certain columns should not have missing (empty) values. These can be unique identifier columns in reference tables (Account ID in the Account Reference, Date in the Calendar) or mandatory columns in actual tables (Amount, Date, etc.). Basic data processing and transformation operations: Verifying steps created automatically in the query editor Deleting columns, selecting columns Filtering rows, deleting rows Removing duplicates based on column values Adding columns, creating duplicate columns, merging columns Changing data types Transposing data Collapsing columns, unpivoting columns Filling values ​​(up/down) Creating function-based queries You can learn more about the program and sign up for the course by following the link - https://vk.cc/c0mHjH More content: FB - https://vk.cc/c0mHnW Instagram - https://vk.cc/c0mHqJ VK - https://vk.cc/c0mHts Telegram - https://vk.cc/c0mHIr