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Data Modeling with Microsoft Power BI - Helion

Data Modeling with Microsoft Power BI
ebook
Autor: Markus Ehrenmueller-Jensen
ISBN: 9781098148515
stron: 484, Format: ebook
Data wydania: 2024-06-10
Księgarnia: Helion

Cena książki: 29,90 zł (poprzednio: 230,00 zł)
Oszczędzasz: 87% (-200,10 zł)

Dodaj do koszyka Data Modeling with Microsoft Power BI

Osoby, które kupiÅ‚y tÄ™ książkÄ™, wybieraÅ‚y także »

Data modeling is the single most overlooked feature in Power BI Desktop, yet it's what sets Power BI apart from other tools on the market. This practical book serves as your fast-forward button for data modeling with Power BI, Analysis Services tabular, and SQL databases. It serves as a starting point for data modeling, as well as a handy refresher.

Author Markus Ehrenmueller-Jensen, founder of Savory Data, shows you the basic concepts of Power BI's semantic model with hands-on examples in DAX, Power Query, and T-SQL. If you're looking to build a data warehouse layer, chapters with T-SQL examples will get you started. You'll begin with simple steps and gradually solve more complex problems.

This book shows you how to:

  • Normalize and denormalize with DAX, Power Query, and T-SQL
  • Apply best practices for calculations, flags and indicators, time and date, role-playing dimensions and slowly changing dimensions
  • Solve challenges such as binning, budget, localized models, composite models, and key value with DAX, Power Query, and T-SQL
  • Discover and tackle performance issues by applying solutions in DAX, Power Query, and T-SQL
  • Work with tables, relations, set operations, normal forms, dimensional modeling, and ETL

Dodaj do koszyka Data Modeling with Microsoft Power BI

 

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Dodaj do koszyka Data Modeling with Microsoft Power BI

Spis treści

Data Modeling with Microsoft Power BI eBook -- spis treści

  • Foreword
  • Preface
    • Who Is This Book For?
    • What Is Data Modeling?
    • What Is Power BI?
    • What Is So Special About a Power BI Data Model?
    • What Is DAX?
    • What Is Power Query?
    • What Is SQL?
    • A New Release Every Few Weeks
    • How to Read This Book
    • Installing Necessary Software
    • Additional Tools
    • Demo Files
    • Conventions Used in This Book
    • Using Code Examples
    • OReilly Online Learning
    • How to Contact Us
    • Acknowledgments
  • I. Data Modeling 101
  • 1. What Is a Data Model?
    • Data Model
    • Basic Components
      • Entity
      • Tables
      • Relationships
      • Primary Keys
      • Surrogate Keys
      • Foreign Keys
      • Cardinality
    • Combining Tables
      • Set Operators
      • Joins
      • Join Path Problems
        • Loop
        • Chasm trap
        • Fan trap
      • Entity Relationship Diagrams
    • Data Modeling Options
      • Types of Tables
      • A Single Table to Store It All
      • Normal Forms
      • Dimensional Modeling
      • Granularity
      • Extract, Transform, Load
      • Ralph Kimball and Bill Inmon
      • Data Vaults and Other Anti-Patterns
    • Key Takeaways
  • 2. Building a Data Model
    • Normalizing
    • Denormalizing
    • Calculations
    • Flags and Indicators
    • Time and Date
    • Role-Playing Dimensions
    • Slowly Changing Dimensions
      • Type 0: Retain Original
      • Type 1: Overwrite
      • Type 2: Add New Row
      • Type 3: Add New Attributes
      • Type 4: Add Mini-Dimensions
      • Types 5, 6, and 7
    • Hierarchies
    • Key Takeaways
  • 3. Real-World Examples
    • Binning
      • Adding a Column to a Fact Table
      • Creating a Lookup Table
      • Describing the Ranges of the Bins
    • Budget
      • Identifying the Granularity
      • Handling Fact Tables of Different Cardinality
    • Multi-Language Model
    • Key-Value Pair Tables
    • Combining Self-Service and Enterprise BI
    • Key Takeaways
  • 4. Performance Tuning
    • Key Takeaways
  • II. Data Modeling in Power BI
  • 5. Understanding a Power BI Data Model
    • Data Model
    • Basic Concepts
      • Tables and Columns
      • Relationships
      • Primary Keys
      • Surrogate Keys
      • Foreign Keys
      • Cardinality
    • Combining Tables
      • Set Operators
      • Joins
      • Join Path Problems
      • Entity Relationship Diagrams
    • Data Modeling Options
      • Types of Tables
      • A Single Table to Store It All
      • Normal Forms
      • Dimensional Modeling
      • Granularity
      • Extract, Transform, Load
    • Key Takeaways
  • 6. Building a Data Model in Power BI
    • Normalizing and Denormalizing
    • Calculations
    • Time and Date
      • Turning off Auto Date/Time
      • Marking the Date Table
    • Role-Playing Dimensions
    • Slowly Changing Dimensions
    • Hierarchies
    • Key Takeaways
  • 7. Real-World Examples Using Power BI
    • Binning
      • Lookup Table
      • Range Table
    • Budget
    • Multi-Language Model
      • Dimension Table for the Available Languages
      • Visual Elements
      • Text-Based Content
      • Numerical Content
      • Data Models Metadata
      • UI of Power BI Desktop (Standalone)
      • UI of Power BI Desktop (Windows Store)
      • UI of the Power BI Service
      • UI of Power BI Report Server
    • Key-Value Pair Tables
    • Combining Self-Service and Enterprise BI
    • Key Takeaways
  • 8. Performance Tuning in the Power BI Data Model
    • Storage Mode
    • Partitioning
    • Pre-Aggregating
    • Composite Models
    • Dual Mode
    • Hybrid Tables
    • Key Takeaways
  • III. Data Modeling for Power BI with the Help of DAX
  • 9. Understanding a Data Model from the DAX Point of View
    • Data Model
    • Basic Components
      • Tables
      • Relationships
      • Primary Keys
    • Combining Queries
      • Set Operators
        • UNION
        • INTERSECT
        • EXCEPT
      • Joins
        • NATURALLEFTOUTERJOIN
        • NATURALINNERJOIN
        • CROSSJOIN
      • Extract, Transform, Load
    • Key Takeaways
  • 10. Building a Data Model with DAX
    • Normalizing
    • Denormalizing
    • Calculations
      • Simple Aggregations for Additive Calculations
      • Semi-Additive Calculations
      • Re-create the Calculation as a DAX Measure
      • Time-Intelligence Calculations
        • Explicit measures
        • Calculation groups
    • Flags and Indicators
      • IF Function
      • SWITCH Function
      • SWITCH TRUE Function
      • Lookup Table
      • Treating BLANK values
    • Time and Date
    • Role-Playing Dimensions
    • Slowly Changing Dimensions
    • Hierarchies
    • Key Takeaways
  • 11. Real-World Examples Using DAX
    • Binning
      • Lookup Table
      • Range Table
    • Budget
    • Multi-Language Model
    • Key-Value Pair Tables
    • Combining Self-Service and Enterprise BI
    • Key Takeaways
  • 12. Performance Tuning with DAX
    • Storage Mode
    • Pre-Aggregating
    • Aggregation-Aware Measures
    • Key Takeaways
  • IV. Data Modeling for Power BI with the Help of Power Query
  • 13. Understanding a Data Model from the Power Query Point of View
    • Data Model
    • Basic Components
      • Tables or Queries
      • Relationships
      • Primary Keys
      • Surrogate Keys
    • Combining Queries
      • Set Operators
      • Joins
      • Query Dependencies
      • Types of Queries
      • Extract, Transform, Load
    • Key Takeaways
  • 14. Building a Data Model with Power Query and M
    • Normalizing
      • Column Quality
      • Column Distribution
      • Column Profile
      • Identifying the Columns to Normalize
      • Creating a Query per Dimension
      • Creating One Common Dimension Query
    • Denormalizing
    • Calculations
    • Flags and Indicators
    • Time and Date
    • Role-Playing Dimensions
    • Slowly Changing Dimensions
    • Hierarchies
    • Key Takeaways
  • 15. Real-World Examples Using Power Query and M
    • Binning
      • Create a Bin Table by Hand
      • Deriving the Bin Table from the Facts
      • Create a Bin Table in M
      • Create a Bin Range Table in M
    • Budget
    • Multi-Language Model
    • Key-Value Pair Tables
      • Using the GUI
      • Using M Code
      • Writing an M Function
    • Combining Self-Service and Enterprise BI
    • Key Takeaways
  • 16. Performance Tuning the Data Model with Power Query
    • Storage Mode
    • Partitioning
    • Pre-Aggregating
    • Key Takeaways
  • V. Data Modeling for Power BI with the Help of SQL
  • 17. Understanding a Relational Data Model
    • Data Model
    • Basic Components
      • Tables
      • Relationships
      • Primary Keys
      • Surrogate Keys
      • Foreign Keys
    • Combining Queries
      • Set Operators
        • UNION
        • INTERSECT
        • EXCEPT
      • Joins
        • INNER JOIN
        • OUTER JOIN
        • Anti-join
        • CROSS JOIN
        • SELF JOIN
        • EQUI-JOIN
        • NON-EQUI-JOIN
        • Natural joins
      • Join Path Problems
        • Loop
        • Chasm trap
        • Fan trap
      • Entity Relationship Diagrams
      • Extract, Transform, Load
    • Key Takeaways
  • 18. Building a Data Model with SQL
    • Normalizing
      • Persisting into a Table
      • Creating a View
      • Creating a Function
      • Creating a Procedure
      • Creating a Filter Dimension
    • Denormalizing
    • Calculations
    • Flags and Indicators
    • Time and Date
    • Role-Playing Dimensions
    • Slowly Changing Dimensions
      • Type 0: Retain Original
      • Type 1: Overwrite
      • Type 2: Add New Row
    • Hierarchies
    • Key Takeaways
  • 19. Real-World Examples Using SQL
    • Binning
      • Deriving the Lookup Table from the Facts
      • Generating a Lookup Table
      • Range Table
    • Budget
    • Multi-Language Model
    • Key-Value Pair Tables
    • Combining Self-Service and Enterprise BI
    • Key Takeaways
  • 20. Performance Tuning the Data Model with SQL
    • Storage Modes
      • Table
      • Index
      • Compression
      • View
      • Function
      • Stored Procedure
    • Partitioning
    • Pre-Aggregating
    • Key Takeaways
  • Epilogue
  • Index

Dodaj do koszyka Data Modeling with Microsoft Power BI

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