![DAX Syntax – Core Rules
Total Population =
Formula Name
• Capital Letters
• Space
DAX Function
Table Reference
• Capital Letters
• No Space
Column Reference
• Square Brackets
• Capital Letters
• No Space
With space in table names, single
quotes are required
Square brackets
always required
SUM ( FactPopulation [ PopulationCount ] )](https://image.slidesharecdn.com/powerbi-240219055543-391ba848/85/PowerBI-importance-of-power-bi-in-data-analytics-field-35-320.jpg)
![DAX Core Functions
Text
Logical
Information
Math
Statistical
Date & Time
Filter
CONCATENATE(“I Love Power”,”BI”) ILove PowerBI
ISNUMBER(2020) TRUE
IF([Population]>100000,“Big“,“Small“) Big Small
ROUND(352.867,2) 352.87
AVERAGE(Dim-Fact[Population])
FILTER(Dim-Fact[Year]=2020)
CALENDAR(DATE(2000,01,01),DATE(2020,12,31))](https://image.slidesharecdn.com/powerbi-240219055543-391ba848/85/PowerBI-importance-of-power-bi-in-data-analytics-field-38-320.jpg)
PowerBI importance of power bi in data analytics field
- 1. Getting Started What & Why?
- 2. What is Power BI? “A business analytics solution that lets you visualize your data and share insights across your organization. Connect to hundreds of data sources and bring your data to life with live dashboards and reports.”
- 3. Three Core Areas Data Preparation & Analysis Visualization Collaboration & Sharing
- 4. Dashboards Collaboration Share Results Datasets Visuals Reports Mobile App Website Login Understanding the Core Power BI Toolset Power BI Desktop Power BI Service (Pro) Power BI Mobile Access Anywhere Windows Only Datasets Visuals Reports
- 5. Course Outline Getting Started Prerequisites The Query Editor Analyzing Data Visualizing Data Power BI Service Power BI Mobile Additional Data Sources How to Stay Updated? Roundup & Next Steps Work locally in Power BI Desktop Collaborate Dive Deeper Master Power BI Bonus: Advanced Features
- 6. How to Get The Most Out Of This Course Watch the Videos Work Along & Do the Exercises At your Speed! Pause & Rewind! Use the Course Resources Attached Project Files & Links Ask in Q&A Help others in Q&A Great Learnings Guaranteed!
- 7. Power BI Desktop Exploring the Desktop Application
- 8. Module Overview How to Use the Attached Project Files Power BI Desktop Workflow Exploring the Data Model The Query Editor Interface Recommended Settings
- 9. Understanding the Workflow Data Preparation Query Editor Data Modeling Data View Relationship View Data Visualization Report View Data Model
- 10. Diving Into The Query Editor Preparing our Dataset
- 11. Module Overview Connecting Power BI Desktop to Files Editing Rows & Columns Appending & Merging Queries Creating a Data Schema (Star Schema) Conditional Columns & Mathematical Operations
- 12. Understanding the Workflow Data Preparation Query Editor Data Modeling Data View Relationship View Data Visualization Report View Data Model
- 13. Understanding Append Germany 100 -20 2014 Query 1 New Query Query 2 Column amount and names must be equal in initial queries! Country Revenue Cost Year Country Revenue Cost Year Germany 100 8 -20 2 2014 5 Germany 108 5 -22 5 2015 6 Germany 105 -25 2016 C Go er u m nt a rn yy Reve1 n1 u0 e C - o 2 s 4 t 2 Y 0 e 1 a 7 r Germany 110 6 -25 2018 Germany 126 2 -27 2019 Germany 122 -27 2019
- 14. Pivoting & Unpivoting Attribute Value Unpivot Pivot Product 2018 2019 2020 Product Attribute Value Apple 2018 10 Apple 2019 12 Apple 2020 13 Banana 2018 23 Banana 2019 25 Banana 2020 21 Product 2018 2019 2020 1 0 1 0 1 2 1 2 1 3 1 3 2 3 2 3 2 5 2 5 2 1 2 1 Product 2018 2019 2020 Apple 10 12 13 Banana 23 25 21
- 15. What we Achieved so Far & How to Continue Source File Connection Row & Column Operations Filters, Formatting, Error Handling Appending Queries Pivoting & Unpivoting Splitting Columns Basic Cleaning & Shaping Develop & Implement our own Data Model
- 16. The Star Schema • Customer-ID • FirstName • SecondName • Age • Gender • Product-ID • Date-ID • Customer-ID • Region-ID • UnitsSold • TotalSales • TotalCost • Date-ID • Year • Quarter • Month • Week • Day • Region-ID • Continent • Country • City • Product-ID • ProductType • PricePerUnit • CostPerUnit DIM TABLE FACT TABLE VS SalesPoint Customers Products Time Sales
- 17. Current Project Structure & Star Schema Population- Combined • Country-ID • Country • Year • Age-Group • Gender • Population DIM Region • Country-ID • Country • Region FACT Population • Country-ID • Age-Group-ID • Year • Gender • Population DIM Age • Age-Group-ID • Age-Group • Category
- 18. Reference vs Duplicate Query Query Query 1 2 3 Combined Query Applied Steps Source Removed Columns … Reference 1 Reference 2 Query Query Query 1 2 3 Combined Query Applied Steps Source Removed Columns … Duplicate 1 Combined Query Applied Steps Source Removed Columns … Duplicate 2 Reference Duplicate
- 19. Merging Queries Theory Query 1 Customer ID Product Price 1 TV 599 7 Notebook 1.699 1 Phone 999 Query 2 Customer ID Name 1 Max 7 Manuel Customer ID Product Price Name 1 TV 599 Max 7 Notebook 1.699 Manuel 1 Phone 999 Max Query 1 +Query 2 Merg e
- 20. Understanding “Join Kind“ ID Sales A 10 B 50 C 20 LEFT QUERY RIGHT QUERY LEFT ID Sales Region A 10 USA B 50 n/a C 20 Asia RIGHT ID Region Sales A USA 10 BB Europe n/a C Asia 20 FULL ID Sales Region A 10 USA B 50 n/a C 20 Asia BB n/a Europe ID Region A USA BB Europe C Asia INNER ID Sales Region A 10 USA C 20 Asia LEFT ID Sales Region B 50 n/a RIGHT ID Region Sales BB Europe n/a OUTER ANTI
- 21. Completing our Star Schema DIM Region • Country-ID • Country • Region FACT Population • Country-ID • Age-Group-ID • Year • Gender • Population DIM Age • Age-Group-ID • Age-Group • Category
- 22. Understanding “Enable Load“ Query 1 Query 2 Combined Query Enable Load Query Editor Data Model Query 1 Query 2 Combined Query Enable Load Query 1 Query 2 Combined Query Combined Query
- 23. Module Summary File Connections Row & Column Operations Filters, Formatting, Error Handling Appending & Merging Queries Pivoting & Unpivoting Splitting Columns & Extracting Values Project Organization (Groups) & Performance Optimization Data Schemas (Star Schema) Duplicates vs References Entering Data Manually Working with Indexes Conditional Columns & Mathematical Operations
- 24. Data View & Relationships Diving Deeper Into Data Analysis
- 25. Module Overview Understanding Relationships M Language vs DAX DAX Introduction Calculated Columns vs Measures Categorizing Data
- 26. Another Look at the Workflow Data Preparation Query Editor Data Modeling Data View Relationship View Data Visualization Report View Data Model
- 27. Query Editor vs. Data Model File Connection Clean Data Shape Data Prepare & Structure Data Query Editor Relationships Calculated Columns Measures Analyse Data Data Model
- 28. Diving Into Relationships Cardinality Cross Filter Direction Active Properties Relationship Type
- 29. One to many (1:*) & Many to one (*:1) Customers ID-Customer FirstName SecondName 1 Maximilian Schwarzmueller 2 John Meyer 3 Linda Belle 4 Manuel Lorenz ID-Order OrderDate ID-Customer A 01 Jan 2020 1 B 08 Jan 2020 2 C 15 Jan 2020 1 D 25 Jan 2020 1 E 05 Feb 2020 3 F 15 Feb 2020 4 Orders Each Customer is Unique Each Customer can have Multiple Orders
- 30. One to one (1:1) ID-Passport Valid Issued FirstName SecondName Country 1 2025 2005 Maximilian Schwarzmueller Germany 2 2021 1999 John Meyer USA 3 2027 1997 Linda Belle Japan ID-Passport FirstName Second Name Country 1 Maximilian Schwarzmueller Germany 2 John Meyer USA 3 Linda Belle Japan ID-Passport Valid Issued 1 2025 2005 2 2021 1999 3 2027 1997 Passport Person
- 31. Diving Into Relationships Cardinality Cross Filter Direction Active Properties Relationship Type
- 32. Diving Into Relationships Cardinality Cross Filter Direction Active Properties Relationship Type
- 33. One Tool - Two Languages Description Where to Apply Power Query Formula Language Data Transformation Data Analysis Expression Language Analytical Data Calculations Comparable to Excel Functions M DAX Data Preparation Create Insights Before Data Model In Data Model
- 34. DAX Basics DAX Reference (Official Docs) https://docs.microsoft.com/en-us/dax/ Syntax Operators DAX Queries DAX Statements Functions Formula = … DEFINE EVALUATE ORDER BY VAR + - CONCATENATE() Basics Advanced Data Types String Number
- 35. DAX Syntax – Core Rules Total Population = Formula Name • Capital Letters • Space DAX Function Table Reference • Capital Letters • No Space Column Reference • Square Brackets • Capital Letters • No Space With space in table names, single quotes are required Square brackets always required SUM ( FactPopulation [ PopulationCount ] )
- 36. DAX Data Types Whole & Decimal Numbers Boolean String (Text) Date/Time Currency Blank (NA) “The DAX Basics“ 564 949.59 TRUE FALSE January 1st 2020
- 37. DAX Operators Arithmetic Comparison Logical + - * / ^ = = = > > = < > && | | IN Text concat. &
- 38. DAX Core Functions Text Logical Information Math Statistical Date & Time Filter CONCATENATE(“I Love Power”,”BI”) ILove PowerBI ISNUMBER(2020) TRUE IF([Population]>100000,“Big“,“Small“) Big Small ROUND(352.867,2) 352.87 AVERAGE(Dim-Fact[Population]) FILTER(Dim-Fact[Year]=2020) CALENDAR(DATE(2000,01,01),DATE(2020,12,31))
- 39. Calculated Columns vs Measures Calculated Column Measure “Perform an operation that generates results for each row of your table“ “Return a single result of a calculation or an aggregated value (e.g. Averages)“
- 40. FILTER & CALCULATE FILTER = <filter> CALCULATE = <filter1> ) ( <expression> , ) ( <table> <filter2> , ,
- 41. Module Summary Query Editor vs Data Model Relationships Cardinality, Cross-Filter-Direction & Active Properties M vs DAX DAX Basics - Theory DAX Basics - Calculated Columns Calculated Columns vs Measures Categorizing Data Combining Measures DAX Basics - Measures
- 42. Report View Diving Into Charts, Tables & More
- 43. Module Overview Creating Visuals & Understanding Reports Filters, Hierarchies & Interactions Chart Formatting
- 44. Another Look at the Workflow Data Preparation Query Editor Data Modeling Data View Relationship View Data Visualization Report View Data Model
- 45. Basic Visual Concepts Legend 2000 2010 Axis 2020 Value Tooltip Total GER USA Senior 32%
- 46. Module Summary Basic Visual Concepts Line, Bar & Column Charts Tooltips & Interactions Hierarchies & Drill Mode Formatting of Visuals Report & Visual Themes The Slicer Custom Visuals Combined Visuals Filter Types
- 47. Power BI Pro & Power BI Mobile Going from Local Projects to the Cloud
- 48. Module Overview Publishing Projects from Power BI Desktop to Power BI Pro (Service) Collaborating in Workspaces Sharing Data with Power BI Pro & Power BI Mobile
- 49. How to Continue IT Marketing Organization Single User Power BI Desktop STOP Power BI Pro (Service) Power BI Mobile Publish Access Power BI Desktop Power BI Pro Power BI Mobile Publish Share Power BI Pro
- 50. Publishing to Power BI Pro Power BI Desktop Our Computer Publish/Connect Power BI Pro Report +Dataset Server Personal Gateway Standard Gateway Power BI Pro
- 51. Sharing Workspaces, Apps & Content Packs Power BI Pro Datasets Reports Dashboard Dataset Report Dashboard Dataset Report Dashboard My (“Your“) Workspace “Your personal cloud workspace“ “Other“ Workspaces “Company-wide collaboration workspace“ App Sharing Content Pack
- 52. Sharing Data: Workspace or App? Workspace App Multiple Developers End-User My Workspace Single Developer
- 53. Module Summary Free vs Pro vs Premium Power BI Pro Interface My Workspace Power BI Pro & Desktop Connection Datasets, Reports & Dashboards Data Refresh with Gateways Collaboration Workspaces & Apps Power BI Mobile
- 55. What you Learned… Power BI Desktop Power BI Pro & Mobile Advanced Data Preparation Publish Data to Pro SQL, JSON, REST APIs Data Models My Workspace Creating Custom Visuals Relationships Workspaces (Collaborate) Column from Examples M & DAX Apps DAX Studio Visuals & Reports Access Anywhere …
- 56. … and How to Continue Repeat unclear Concepts Redo the Project on your own Create own Projects –the Web is full of Amazing Data to Discover Dive into the Official Docs Stay up-to-date