Power BI与DAX入门：商业智能新指南

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"《 Beginning DAX with Power BI》是一本由Philip Seamark编著的针对Power BI的数据访问编程指南，旨在让商业智能（BI）工具的使用变得更加简单易懂，适用于业务分析师。这本书以实际商业场景为背景，将复杂的DAX（Data Analysis Expressions）语言概念拆解为简单的步骤，帮助读者解决日常业务中的实际问题。" 本书针对Power BI用户，特别是那些对BI有一定了解但希望深入学习DAX语言的读者。DAX是Power BI、Power Pivot和Analysis Services等微软BI工具中用于创建计算列和度量值的强大表达式语言。通过学习DAX，用户能够更高效地分析和处理数据，实现更高级的数据洞察。书中内容可能涵盖以下几个方面： 1. **DAX基础**：介绍DAX的基本语法，包括函数、运算符、表达式和变量，以及如何构建计算列和度量值。 2. **数据建模**：探讨如何在Power BI中建立有效的数据模型，包括关系的创建、表的连接方式以及星型和雪花型模式的应用。 3. **时间智能**：详细讲解如何使用DAX处理时间序列数据，实现滚动平均、趋势分析等常见的时间智能功能。 4. **聚合与过滤**：解释如何使用DAX进行数据的聚合和过滤操作，如SUMX、AVERAGEIF等，以及理解上下文转换的概念。 5. **性能优化**：讨论如何提高DAX查询的执行效率，避免计算性能瓶颈，以及利用Power BI Desktop的查询优化器来监控和改进模型性能。 6. **自定义函数和计算**：介绍如何创建自定义DAX函数，以及利用这些函数解决特定的业务需求。 7. **案例研究**：提供真实世界的业务场景，通过解决这些场景中的问题，帮助读者将理论知识应用到实践中。 8. **最佳实践**：分享在使用DAX时应遵循的最佳实践，以确保代码的可读性、可维护性和扩展性。 9. **集成与发布**：讲解如何将DAX模型与Power BI报告、Power Pivot for Excel以及SharePoint等平台集成，并进行发布和共享。 10. **持续学习与资源**：推荐进一步学习DAX和Power BI的资源，如在线社区、文档和技术论坛，帮助读者持续提升技能。通过阅读这本书，读者将能够掌握DAX语言，提升在Power BI环境中构建和优化数据分析模型的能力，从而在企业自我服务BI文化中发挥关键作用，推动业务决策的智能化。

What Is aData Model?

A data model is a collection of data, calculations, and formatting rules that combine to

create an object that can be used to explore, query, and better understand an existing

dataset. This can include data from many sources.

Power BI Desktop, SSAS Tabular, and PowerPivot for Excel can import data from

a wide variety of data sources including databases and flat files or directly from many

source systems. Once imported, calculations can be added to the model to help explore

and make sense of the data.

Data is organized and stored into tables. Tables are two dimensional and share many

characteristics with databases tables. Tables have columns and rows, and relationships

can be defined between tables to assist calculations that use data from multiple tables.

Calculations can be as simple as providing a row count over a table or providing a sum of

values in a column. Well-considered calculations should enhance your data model and

support the process of building reports and performing analytical tasks known as measures.

It’s the combination of data and measures that become your data model.

The Power BI Desktop user interface consists of three core components. First, the

Report View provides a canvas that lets you create a visual layer of your data model

using charts and other visuals. Report View also lets you control the layout by dragging,

dropping, and resizing elements on the report canvas. It’s the canvas that is presented to

the end user when they access the report.

The second component is the Data View, which provides the ability to see raw

data for each table in the model. Data View can show data for one table at a time and is

controlled by clicking the name of a table from the list of tables in the right-hand panel.

Columns can be sorted in this view, but sorting here has no impact on any sorting by

visuals on the report canvas. Columns can be renamed, formatted, deleted, hidden, or

have their datatype defined using the Report View. A hidden column will always appear

in the Report View but not in any field list in the report.

It’s possible to add or change calculations from both Report and Data View.

The last component of the Power BI Desktop user interface is the Relationship View.

This section shows every table in the data model and allows you to add, change, or

remove relationships between tables.

CHAPTER 1 INTRODUCTION TO DAX

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and recalculate as a report is being used. Measures can return new values based on

updates to the selection of filters and slicers.

Hierarchies

Hierarchies are groupings of two or more columns into levels that can be drilled up/

down through by interactive visuals and charts. A common hierarchy might be over

date data that creates a three-level hierarchy over year, month, and day. Other common

hierarchies might use geographical data (country, city, suburb), or structures that reflect

organizational groupings in HR or Product data.

Your First DAX Calculation

It’s possible to import data and have no need to write in DAX. If data is clean and simple

and report requirements are basic, you can create a model that needs no user-created

calculations. Numeric fields dragged to the report canvas will produce a number.

If you then drag a non-numeric field to the same visual, it automatically assumes you

would like to group your numeric field by the distinct values found in your nonnumeric

field. The default aggregation over your numeric field will be SUM.This can be changed

to another aggregation type using the properties of your visual. Other aggregation types

include AVERAGE, COUNT, MAX, MIN and so on.

In this approach, the report creates a DAX-calculated measure on your behalf. These

are known as implicit measures. Dragging the ‘Fact Sale’[Quantity] field to the canvas

automatically generates the following DAX statement for you:

CALCULATE(SUM('Fact Sale'[Quantity]))

This calculation recomputes every time a slicer or filter is changed and should show

values relevant for any filter settings in your report.

Most real-world scenarios require at least some basic enhancements to raw data, and

this is where adding DAX calculations can improve your model. When you specifically

create calculated measures, these are known as explicit measures.

Some of the most common enhancements are to provide the ability to show a count

of the number of records in a table or to sum values in a column.

Other enhancements might be to create a new column using values from other

columns in the same row, or from elsewhere in the model. A simple example is a

column that multiplies values from columns such as Price and Qty together to produce

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