Power BI实战指南：2017版

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"Beginning Power BI, 2nd Edition" 是一本由Dan Clark编著的书籍，专注于介绍如何使用Power BI和Excel 2016进行自助式数据分析。这本书共有348页，属于第2版，由Apress出版，出版日期为2017年4月1日，ISBN号码为9781484225769。全书分为两个部分，分别涵盖了在Power Pivot中构建数据模型和使用Power BI Desktop创建交互式报告和仪表板。第一部分“Building Models in Power Pivot”（在Power Pivot中构建模型）包括以下章节： 1. 第1章介绍了Power Pivot，解释了这个工具的基础和它在自助式数据分析中的角色。 2. 第2章详细阐述如何将数据导入Power Pivot，包括各种数据源的连接和管理。 3. 第3章讨论了Power Query，它是清洗和转换数据的强大工具，帮助用户准备分析数据。 4. 第4章展示了如何创建数据模型，这是数据分析的关键组成部分，用于组织和关联不同数据表。 5. 第5章和第6章深入讲解了DAX（数据分析表达式），用于创建计算字段和度量值，是Power Pivot中的重要计算语言。 6. 第7章专门介绍了时间智能功能，这使得用户能够轻松地分析基于时间的数据。 7. 第8章讨论如何使用透视表和图表进行数据洞察，这是数据分析和报告展示的常用方法。第二部分“Building Interactive Reports and Dashboards with Power BI Desktop”（使用Power BI Desktop构建交互式报告和仪表板）： 9. 第9章引入了Power BI Desktop，解释了其与Power Pivot的区别以及在创建报告上的优势。 10. 第10章指导读者如何使用Power BI Desktop创建富有视觉吸引力的报告。 11. 第11章介绍了如何在Power BI门户中发布报告并创建仪表板，实现数据共享和协作。 12. 第12章展示了如何构建一个完整的解决方案，涵盖了从数据集成到报告交付的全过程。 13. 第13章和第14章涵盖了Power Query和Power BI的高级主题，包括更复杂的查询和高级分析技术。这本书不仅适合初学者，也对有一定经验的Power BI用户提供了有价值的进阶内容。通过学习，读者可以掌握利用Excel 2016和Power BI Desktop进行高效数据处理、分析和可视化的方法，从而提升自身的自助式数据分析能力。

D. Clark, Beginning Power BI, DOI 10.1007/978-1-4842-2577-6_1

CHAPTER 1

Introducing Power Pivot

The core of Microsoft’s self-service business intelligence (BI) toolset is Power Pivot. It is integrated into both

Excel 2016 and Power BI and forms the foundation on top of which you will build your analytical reports and

dashboards. This chapter provides you with some background information on why Power Pivot is such an

important tool and what makes it perform so well. The chapter also provides you with an overview of the Power

Pivot interface in Excel 2016 and gives you with some experience using the different areas of the interface.

After reading this chapter you will be familiar with the following:

• Why you should use Power Pivot

• The xVelocity in-memory analytics engine

• Exploring the Data Model Management interface

• Analyzing data using a pivot table

Why Use Power Pivot?

You may have been involved in a traditional BI project consisting of a centralized data warehouse where

the various data stores of the organization are loaded, scrubbed, and then moved to an online analytical

processing (OLAP) database for reporting and analysis. Some goals of this approach are to create a data

repository for historical data, create one version of the truth, reduce silos of data, clean the company data

and make sure it conforms to standards, and provide insight into data trends through dashboards. Although

these are admirable goals and are great reasons to provide a centralized data warehouse, there are some

downsides to this approach. The most notable is the complexity of building the system and implementing

change. Ask anyone who has tried to get new fields or measures added to an enterprise-wide data

warehouse. Typically, this is a long, drawn-out process requiring IT involvement along with data steward

committee reviews, development, and testing cycles.

What is needed is a solution that allows for agile data analysis without so much reliance on IT and

formalized processes. To solve these problems, many business analysts have used Excel to create pivot tables

and perform ad hoc analysis on sets of data gleaned from various data sources. Some problems with using

isolated Excel workbooks for analysis are conflicting versions of the truth, silos of data, and data security.

So how can you solve this dilemma of the centralized data warehouse being too rigid while the Excel

solution is too loose? This is where Microsoft’s self-service BI toolset comes in. These tools do not replace

your centralized data warehouse solution but rather augment it to promote agile data analysis. Using Power

Pivot, you can pull data from the data warehouse, extend it with other sources of data such as text files or

web data feeds, build custom measures, and analyze the data using pivot tables and pivot charts. You can

create quick proofs of concepts that can be easily promoted to become part of the enterprise-wide solution.

Power Pivot also promotes one-off data analysis projects without the overhead of a drawn-out development

Chapter 1 ■ IntroduCIng power pIvot

cycle. When combined with SharePoint 2016, SQL Server Reporting Services 2016, and/or One Drive,

workbooks can be shared, secured, and managed. This goes a long way to satisfying IT’s need for governance

without impeding the business user’s need for agility.

Here are some of the benefits of Power Pivot:

• Functions as a free add-in to Excel

• Easily integrates data from a variety of sources

• Handles large amounts of data, upward of tens to hundreds of millions of rows

• Uses familiar Excel pivot tables and pivot charts for data analysis

• Includes a powerful Data Analysis Expressions (DAX) language

• Has data in the model that is read only, which increases security and integrity

When Excel Power Pivot workbooks are hosted in SharePoint, Reporting Services, and/or One Drive,

here are some of its added benefits:

• Enables the sharing and collaboration of Excel Power Pivot solutions

• Can schedule and automate data refresh

• Can audit changes through version management

• Can secure users for read-only and updateable access

Now that you know some of the benefits of Power Pivot, let’s see what makes it tick.

The xVelocity In-memory Analytics Engine

The special sauce behind Power Pivot is the xVelocity in-memory analytics engine (yes, that is really the

name). xVelocity allows Power Pivot to provide fast performance on large amounts of data. One of the keys to

this is it uses a columnar database to store the data. Traditional row-based data storage stores all the data in

the row together and is efficient at retrieving and updating data based on the row key, for example, updating

or retrieving an order based on an order ID. This is great for the order-entry system but not so great when you

want to perform analysis on historical orders (say you want to look at trends for the past year to determine

how products are selling, for example). Row-based storage also takes up more space by repeating values for

each row; if you have a large number of customers, common names like John or Smith are repeated many

times. A columnar database stores only the distinct values for each column and then stores the row as a set

of pointers back to the column values. This built-in indexing saves a lot of space and allows for significant

optimization when coupled with data-compression techniques that are built into the xVelocity engine. It also

means that data aggregations (like those used in typical data analysis) of the column values are extremely fast.

Another benefit provided by the xVelocity engine is the in-memory analytics. Most processing

bottlenecks associated with querying data occur when data is read from or written to a disk. With in-memory

analytics, the data is loaded into the RAM memory of the computer and then queried. This results in

much faster processing times and limits the need to store pre-aggregated values on disk. This advantage

is especially apparent when you move from 32-bit to 64-bit operating systems and applications, which are

becoming the norm these days.

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