Jeremey Arnold
Learning Microsoft Power BI
Transforming Data Into Insights
Paperback Engels 2022 1e druk 9781098112844Levertijd ± 16 werkdagen
Gratis verzonden
Samenvatting
Microsoft Power BI is a data analytics and visualization tool powerful enough for the most demanding data scientists, but accessible enough for everyday use for anyone who needs to get more from data. The market has many books designed to train and equip professional data analysts to use Power BI, but few of them make this tool accessible to anyone who wants to get up to speed on their own.
This streamlined intro to Power BI covers all the foundational aspects and features you need to go from "zero to hero" with data and visualizations. Whether you work with large, complex datasets or work in Microsoft Excel, author Jeremey Arnold shows you how to teach yourself Power BI and use it confidently as a regular data analysis and reporting tool.
You'll learn how to:
- Import, manipulate, visualize, and investigate data in Power BI
- Approach solutions for both self-service and enterprise BI
- Use Power BI in your organization's business intelligence strategy
- Produce effective reports and dashboards
- Create environments for sharing reports and managing data access with your team
- Determine the right solution for using Power BI offerings based on size, security, and computational needs
Specificaties
ISBN13:9781098112844
Taal:Engels
Bindwijze:paperback
Aantal pagina's:250
Uitgever:O'Reilly
Druk:1
Verschijningsdatum:28-12-2022
Hoofdrubriek:IT-management / ICT
Lezersrecensies
Wees de eerste die een lezersrecensie schrijft!
Inhoudsopgave
Preface
Navigating This Book
Conventions Used in This Book
Using Code Examples
O'Reilly Online Learning
How to Contact Us
Acknowledgments
1. Intro to Power BI
What Is Power BI?
Power BI Components
Power BI Desktop
The Power BI Service
The Power Platform
How Did We Get to Power BI?
SQL Server: Microsoftâs Relational Database
SQL Server Analysis Services Multidimensional: One Small Step into BI
SQL Server Reporting Services: Pixel-Perfect Reporting, Automated Reports, and More
Excel: A Self-Service BI Tool
Power Pivot
Important New Functionality That Leads to Power BI
Power BI Desktop Is Born
Power BI Desktop Under the Hood
VertiPaq: The Storage Engine
DAX: The Formula Engine
What Makes Power BI Different from Its Competitors?
Conclusion
2. The Report and Data Views
Report View: Home Section of the Ribbon
The Clipboard Subsection
The Data Subsection
The Queries Subsection
The Insert Subsection
The Calculations Subsection
The Final Subsections: Sensitivity and Share
Report View: The Insert Tab
The Pages Subsection
The Visuals Subsection
The AI Visuals Subsection
The Power Platform Subsection
The Elements Subsection
Report View: The Modeling Tab
The Relationships Subsection
The Calculations Subsection
The Page Refresh Subsection
The What If Subsection
The Security Subsection
The Q&A Subsection
Report View: The View Tab
The Themes Subsection
The Scale to Fit Subsection
The Page Options Subsection
The Show Panes Subsection
Report View: Help Section
Report View: External Tools Section
The Pane Interface of the Report View
Visualizations Pane
Fields and Filters Panes
A Quick Rundown of the Other Panes
Data View
Conclusion
Navigating This Book
Conventions Used in This Book
Using Code Examples
O'Reilly Online Learning
How to Contact Us
Acknowledgments
1. Intro to Power BI
What Is Power BI?
Power BI Components
Power BI Desktop
The Power BI Service
The Power Platform
How Did We Get to Power BI?
SQL Server: Microsoftâs Relational Database
SQL Server Analysis Services Multidimensional: One Small Step into BI
SQL Server Reporting Services: Pixel-Perfect Reporting, Automated Reports, and More
Excel: A Self-Service BI Tool
Power Pivot
Important New Functionality That Leads to Power BI
Power BI Desktop Is Born
Power BI Desktop Under the Hood
VertiPaq: The Storage Engine
DAX: The Formula Engine
What Makes Power BI Different from Its Competitors?
Conclusion
2. The Report and Data Views
Report View: Home Section of the Ribbon
The Clipboard Subsection
The Data Subsection
The Queries Subsection
The Insert Subsection
The Calculations Subsection
The Final Subsections: Sensitivity and Share
Report View: The Insert Tab
The Pages Subsection
The Visuals Subsection
The AI Visuals Subsection
The Power Platform Subsection
The Elements Subsection
Report View: The Modeling Tab
The Relationships Subsection
The Calculations Subsection
The Page Refresh Subsection
The What If Subsection
The Security Subsection
The Q&A Subsection
Report View: The View Tab
The Themes Subsection
The Scale to Fit Subsection
The Page Options Subsection
The Show Panes Subsection
Report View: Help Section
Report View: External Tools Section
The Pane Interface of the Report View
Visualizations Pane
Fields and Filters Panes
A Quick Rundown of the Other Panes
Data View
Conclusion
3. Importing and Modeling Our Data
Getting Our Data
The Power Query Ribbon
The Home Tab
The Transform Tab
The Add Column Tab
The Model View
What Is a Relationship?
The Properties Pane
Conclusion
4. Let's Make Some Pictures (Visualizing Data 101)
Why Visualize Data?
The Visualizations Pane
Fields
Format
Analytics
Visual Interactivity
Column and Bar Charts
Stacked Bar and Column Charts
Clustered Bar and Column Charts
100% Stacked Bar and Column Charts
Small Multiples
Waterfall Chart
Line and Area Charts
Line Chart
Area Chart
Stacked Area Chart
Line and Stacked Column Chart/Clustered Column Chart
Ribbon Chart
Donuts, Dots, and Maps, Oh My!
Funnel Chart
Scatter Chart
Pie and Donut Chart
Treemap
Map Visuals
The 'Flat' Visuals
Gauge
Card/Multi-Row Card
KPI
Table/Matrix
Slicer
Conclusion
5. Aggregations, Measures, and DAX
A Primer on the DAX Language
Measures
Calculated Columns
Calculated Tables
Types of Functions
Aggregations, More Than Some Sums
Sum
Average
Minimum and Maximum
Standard Deviation, Variance, and Median
Count and Count (Distinct)
First, Last, Earliest, and Latest
Measures and DAX Fundamentals
Implicit and Explicit Measures
DAX Syntax Fundamentals
CALCULATE
We Heard You Like DAX, So We Put Some DAX in Your DAX
Row and Filter Context
One Final DAX Example
Conclusion
6. Putting the Puzzle Pieces Together: From Raw Data to Report
Your First Data Import
Choose and Transform the Data When You Import
Transformations in Power Query
Second Data Import and Wrangling
Consolidating Tables with Append
Using Merge to Get Columns from Other Tables
Building Relationships
Hiding Tables
Identifying Our Relationship Columns
Time to Get Building
Let's Get Reporting!
We Need a Name...
Cards Help Identify Important Data Points
Bars, Columns, and Lines
Conclusion
7. Advanced Reporting Topics in Power BI
AI-Powered Visuals
Key Influencers
Decomposition Tree
Q&A
Smart Narrative
What-If Analysis
Parameter Setup
DAX Integration of the Parameter
Parameter Modification
R and Python Integration
Limitations of Using R and Python
Enabling R and Python for Power BI
R and Python in Power Query
R and Python Visuals
Conclusion
8. Introduction to the Power BI Service
The Basics of the Service: What You Need to Know
The Navigation Menu
Home and Browse
Create
Data Hub
Settings
Metrics
Apps
Deployment Pipelines
Learn
Publishing Your Work
What Is a Workspace?
My Workspace
Shared Capacity Workspaces
Dataflows in Shared Workspaces
Putting Your Data in Front of Others
Adding Users to a Workspace
Sharing via a Link or Teams
Sharing via SharePoint
Creating an App
Conclusion
9. Licensing and Deployment Tips
Licensing
Pro Licensing
Premium Per User Licensing
Premium Per Capacity, the Big Boy
Workspace and App Management
Workspace Generation and Access Control
Managing Users in a Workspace
Adding Users to Roles for RLS Implementation
App Creation and Management
The Golden Dataset(s)
Conclusion
10. Third-Party Tools
Get to Know Business Ops
Add External Tools, Remove External Tools, and Modify Display Order
Learning, Theme Generation, Visual Generation
Additional DAX Resources
DAX Studio
Tabular Editor
Creating Roles
Table and Measure Management
The ALM Toolkit for Power BI
Bravo
Analyze Model
DAX Formatting
Manage Dates
Export Data
Conclusion
A. Commonly Used DAX Expressions
B. Some Favorite Custom Visuals
Index
About the Author
Getting Our Data
The Power Query Ribbon
The Home Tab
The Transform Tab
The Add Column Tab
The Model View
What Is a Relationship?
The Properties Pane
Conclusion
4. Let's Make Some Pictures (Visualizing Data 101)
Why Visualize Data?
The Visualizations Pane
Fields
Format
Analytics
Visual Interactivity
Column and Bar Charts
Stacked Bar and Column Charts
Clustered Bar and Column Charts
100% Stacked Bar and Column Charts
Small Multiples
Waterfall Chart
Line and Area Charts
Line Chart
Area Chart
Stacked Area Chart
Line and Stacked Column Chart/Clustered Column Chart
Ribbon Chart
Donuts, Dots, and Maps, Oh My!
Funnel Chart
Scatter Chart
Pie and Donut Chart
Treemap
Map Visuals
The 'Flat' Visuals
Gauge
Card/Multi-Row Card
KPI
Table/Matrix
Slicer
Conclusion
5. Aggregations, Measures, and DAX
A Primer on the DAX Language
Measures
Calculated Columns
Calculated Tables
Types of Functions
Aggregations, More Than Some Sums
Sum
Average
Minimum and Maximum
Standard Deviation, Variance, and Median
Count and Count (Distinct)
First, Last, Earliest, and Latest
Measures and DAX Fundamentals
Implicit and Explicit Measures
DAX Syntax Fundamentals
CALCULATE
We Heard You Like DAX, So We Put Some DAX in Your DAX
Row and Filter Context
One Final DAX Example
Conclusion
6. Putting the Puzzle Pieces Together: From Raw Data to Report
Your First Data Import
Choose and Transform the Data When You Import
Transformations in Power Query
Second Data Import and Wrangling
Consolidating Tables with Append
Using Merge to Get Columns from Other Tables
Building Relationships
Hiding Tables
Identifying Our Relationship Columns
Time to Get Building
Let's Get Reporting!
We Need a Name...
Cards Help Identify Important Data Points
Bars, Columns, and Lines
Conclusion
7. Advanced Reporting Topics in Power BI
AI-Powered Visuals
Key Influencers
Decomposition Tree
Q&A
Smart Narrative
What-If Analysis
Parameter Setup
DAX Integration of the Parameter
Parameter Modification
R and Python Integration
Limitations of Using R and Python
Enabling R and Python for Power BI
R and Python in Power Query
R and Python Visuals
Conclusion
8. Introduction to the Power BI Service
The Basics of the Service: What You Need to Know
The Navigation Menu
Home and Browse
Create
Data Hub
Settings
Metrics
Apps
Deployment Pipelines
Learn
Publishing Your Work
What Is a Workspace?
My Workspace
Shared Capacity Workspaces
Dataflows in Shared Workspaces
Putting Your Data in Front of Others
Adding Users to a Workspace
Sharing via a Link or Teams
Sharing via SharePoint
Creating an App
Conclusion
9. Licensing and Deployment Tips
Licensing
Pro Licensing
Premium Per User Licensing
Premium Per Capacity, the Big Boy
Workspace and App Management
Workspace Generation and Access Control
Managing Users in a Workspace
Adding Users to Roles for RLS Implementation
App Creation and Management
The Golden Dataset(s)
Conclusion
10. Third-Party Tools
Get to Know Business Ops
Add External Tools, Remove External Tools, and Modify Display Order
Learning, Theme Generation, Visual Generation
Additional DAX Resources
DAX Studio
Tabular Editor
Creating Roles
Table and Measure Management
The ALM Toolkit for Power BI
Bravo
Analyze Model
DAX Formatting
Manage Dates
Export Data
Conclusion
A. Commonly Used DAX Expressions
B. Some Favorite Custom Visuals
Index
About the Author
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