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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-modelling-in-power-bi/
课程评论：没有评论
**课程名称：** Power BI 中的数据建模 **课程概述：** Power BI 数据建模是数据分析和报表创建的基石。本课程将引导你掌握一系列数据清洗、塑造和加载的实用技巧，这些技巧能够有效地应用于你的数据。 在 Power BI 中创建复杂的数据模型并非难事。然而，当数据源自多个事物系统时，你可能会面临数十张需要处理的表。**构建一个优秀的数据模型，其核心在于化繁为简，解决数据杂乱的问题。** 一个出色的数据模型是数据分析师在 Microsoft Power BI 中最重要的任务之一。通过做好这一点，你将帮助用户更轻松地理解数据，从而让你自己和他人更方便地构建有价值的 Power BI 报表。 为“好的数据模型”设定明确的规则是困难的，因为数据各不相同，数据的用途也各不相同。**一般来说，越小的数据模型越好，因为它性能更优越，使用起来也更简单。** 然而，定义“小”数据模型同样具有挑战性，因为它是一个经验性且主观的概念。 通常，一个较小的数据模型包含更少的表，并且每个表中的用户可见列数也更少。例如，如果你从一个销售数据库导入了 30 张表，用户可能不会觉得直观。将这 30 张表合并成 5 张表，将使数据模型对用户更加友好。反之，如果用户打开一张表，发现有 100 列，他们可能会感到不知所措。**移除不必要的列，提供一个更易于管理的列数，将增加用户阅读所有列名的可能性。** **总而言之，在设计数据模型时，应始终追求简洁。**
Proper data modelling is the foundation of data analysis and creating reports in Power BI. This course lets you explore a toolbox of data cleaning, shaping, and loading techniques, which you can apply to your data.The process of creating a complicated data model in Power BI is straightforward. If your data is coming in from more than one transactional system, before you know it, you can have dozens of tables that you have to work with. Building a great data model is about simplifying the disarray.Creating a great data model is one of the most important tasks that a data analyst can perform in Microsoft Power BI. By doing this job well, you help make it easier for people to understand your data, which will make building valuable Power BI reports easier for them and for you.Providing set rules for what makes a good data model is difficult because all data is different, and the usage of that data varies. Generally, a smaller data model is better because it will perform faster and will be simpler to use. However, defining what a smaller data model entails is equally as problematic because it's a heuristic and subjective concept.Typically, a smaller data model is comprised of fewer tables and fewer columns in each table that the user can see. If you import all necessary tables from a sales database, but the total table count is 30 tables, the user will not find that intuitive. Collapsing those tables into five tables will make the data model more intuitive to the user, whereas if the user opens a table and finds 100 columns, they might find it overwhelming. Removing unneeded columns to provide a more manageable number will increase the likelihood that the user will read all column names. To summarize, you should aim for simplicity when designing your data models.
