Tableau Prep: Up & Running Carl Allchin

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Spis treści książki

• Preface
  • Why I Wrote This Book
  • Who This Book Is For
  • How This Book Is Organized
  • Acknowledgments
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
• 1. Why Self-Service Data Prep?
  • A Short History of Self-Service Data Visualization
  • Accessing the Right Data
  • The Self-Service Data Preparation Opportunity
  • Tableau Prep Up and Running
  • Summary
• I. Getting Started
• 2. Getting Started with Tableau Prep Builder
  • Where to Get Tableau Prep Builder
  • How to Get a License for Prep Builder
  • The Tableau Prep Builder Screen
  • Basic Steps of Data Preparation
    • Input Step
    • Clean Step
    • Output Step
    • Saving a Flow
  • Summary
• 3. Planning Your Prep
  • Stage 1: Know Your Data
  • Stage 2: Identify the Desired State
  • Stage 3: Determine the Required Transitions from KYD to the Desired State
  • Stage 4: Build the Workflow
  • Summary
• 4. Shaping Data
  • What to Look for in Incoming Data Sets
  • What Shape Is Best for Analysis in Tableau?
  • Changing Data Set Structures in Prep Builder
    • Pivot
    • Aggregate
    • Join
    • Union
  • Applying Restructuring Techniques to the Ice Cream Example
    • Step 1: Pivot Columns to Rows
    • Step 2: Pivot Rows to Columns
  • Summary
• 5. Connecting to Data in Files
  • Files Upon Files Upon Files
    • Spreadsheets
    • Other File Types
  • Where to Find Your Data Files
  • How to Connect to Files in Prep
  • Considerations for Saving Flows with File Inputs
  • Summary
• 6. Connecting to a Database
  • What Is a Database?
  • How to Connect to a Database Within Prep Builder
  • When to Avoid Connecting to a Database
  • Summary
• II. Data Types
• 7. Dealing with Numbers
  • What Do We Mean by Numbers?
  • Types of Numbers
  • Category or Measure?
  • Aggregation
  • Formatting Numbers
  • Functions for Mastering Numerical Data
  • Summary
• 8. Dealing with Dates
  • Why Are Dates Important?
  • Parts of a Date
  • Date Lookup Tables
  • Epoch Dates
  • Excel Serial Number
  • Entering Dates
    • The makedate() Function
    • The dateparse() Function
  • Summary
• 9. Dealing with String Data
  • What Do We Mean by Strings?
  • How String Data Is Different
    • Character Order
    • Formatting Considerations
      • Names
      • Case sensitivity
      • Addresses
      • Spaces
      • Poor or inconsistent spelling
  • Common Functions for Preparing String Data
  • Grouping and Replace Options for Working with String Data
  • Summary
• 10. Dealing with Boolean Data
  • What Is Boolean Data?
    • Why Is It So Useful in Data Analysis?
    • Functions Featuring Boolean Logic
      • IIF()
      • contains()
      • IsDate()
      • IsNull()
      • IF/THEN
      • CASE
  • Summary
• III. The Shape of Data
• 11. Profiling Data
  • What Is a Profile?
  • Why Visualizing the Data Set Is Important
    • Anscombes Quartet
    • Visualizations Versus Data Tables
  • How Prep Builder Profiles Data
    • Generating Histograms and Mini-Histograms
    • Selecting Summary Versus Detail Views
    • Highlighting Values
    • Viewing Dimension Counts
  • Sorting
  • Summary
• 12. Sampling Data Sets
  • One Simple Rule: Use It All If Possible
  • Sampling to Work Around Technical Limitations
    • Volume of Data
    • Velocity of Data
  • Other Reasons for Sampling
    • Reduce Build Times
    • Determine What You Need
  • Sampling Techniques
    • Fixed Number of Rows
    • Random Sample
  • When Not to Sample
  • Summary
• 13. Pivoting Columns to Rows
  • When to Pivot in Tableau Prep Builder
  • How to Pivot Columns to Rows
  • Summary
• 14. Pivoting Rows to Columns
  • When to Use a Rows-to-Columns Pivot
  • How to Pivot Rows to Columns
  • Summary
• 15. Aggregating in Prep Builder
  • Comparing Calculations in Prep Builder and Desktop
  • Which Calculations in Prep Builder Differ?
  • Adding the Aggregate Step
  • Wheres the Rest of My Data?
  • Level of Detail Calculation Option
  • Summary
• 16. Joining Data Sets Together
  • How to Join Data Sets in Prep Builder
  • Join Logic and Terminology
  • Types of Join in Prep Builder
  • When to Use Each Join Type
  • Summary
• 17. Unioning
  • What Is a Union?
  • What If the Data Structure Isnt Identical?
  • When to Union Data
    • Monthly Data Sets
    • Data Sets from Web Sources
    • Company Mergers
  • Multiple Tables and Wildcard Unions
  • Summary
• 18. Calculations
  • What Do Calculations Do in Data Preparation?
  • Creating a Calculated Field
  • Fundamentals of Calculations
    • The Reference List
    • Syntax
    • Description
    • Example
  • Building the Calculation
    • When Calculations Go Well
    • When Calculations Go Poorly
    • Editing Calculated Fields
    • Recommendations
  • Types of Calculations
    • Numerical Calculations
    • String Calculations
    • Date Calculations
    • Conditional Calculations with a Boolean Output
    • Logical Calculations
    • Type Conversions
  • Level of Detail and Ranking Calculations
  • Summary
• IV. Output
• 19. Choosing an Output
  • Types of Output
    • Publish to Files
      • Hyper files
      • TDE files
      • CSV files
    • Publish to Tableau Server
  • When to Output Data in Prep Builder
    • Outputting Data in the Output Step
      • Save to file
      • Publish as a data source
    • Previewing Output Data in Desktop
  • Other Considerations for Output Data
  • Summary
• 20. Outputting to a Database
  • When to Write to a Database
    • Clean Data
    • Simplified Joins
    • Staging and Reference Tables
  • Setup for Writing to a Database
  • What to Watch Out For
  • Summary
• 21. Getting Started with Tableau Prep Conductor
  • When to Use Prep Conductor
  • How to Get Prep Conductor
  • Loading a Flow to Prep Conductor
  • Other Benefits of Using Prep Conductor
  • Summary
• V. Cleaning Data
• 22. Creating Additional Data
  • When Not to Create Data
    • Dynamic Calculations in Desktop
    • Duplicate Records from Joins
  • Creating Additional Columns
    • Using Calculations
    • Pivoting Rows to Columns
    • Joining Data Sets
  • Creating Additional Rows
    • Pivoting Columns to Rows
    • Unioning Data Sets
    • Scaffolding Data Sets
    • Joining Data Sets
  • Summary
• 23. Filtering
  • What Is a Filter?
  • Different Types of Filters
    • Selection
      • In the Profile pane
      • In the Data pane
      • From a data field
    • Calculation
    • Wildcard
    • Null Values
  • When to Filter Out Columns
  • When to Filter Out Rows
  • Summary
• 24. Removing Data During Input
  • Changing Your Data Set Before Loading It
  • Slow Performance, Slow Build, Slow Output
  • Removing Columns
  • Removing Records
  • Summary
• 25. Splitting Data Fields
  • Basic Splits
  • Advanced Splits: When Automatic Splits Dont Work as Intended
  • When Not to Split Data
    • Address Data
    • No Clear Delimiter
  • Summary
• 26. Cleaning by Grouping Data
  • What Does Grouping Mean?
  • Why Use Grouping
    • Improving Accuracy
    • Navigating the Data Hierarchy
    • Smoothing Reorganizations
  • Grouping Techniques
    • Manual
    • Calculations
      • IF statements
      • REPLACE() functions
    • Built-in Functionality
  • Summary
• 27. Dealing with Nulls
  • What Is a Null?
  • When Is a Null OK?
  • How to Remove or Replace a Null
    • ISNULL()
    • ZN()
    • Merge
  • Summary
• 28. Using Data Roles
  • How to Use Data Roles
  • Custom Data Roles
  • Summary
• 29. Dealing with Unwanted Characters
  • What Is an Unwanted Character?
  • Issues Caused by Unwanted Characters
  • Removing Unwanted Characters
    • Strings with Mistyped Characters
    • Numbers with Unwanted Characters
    • Dates with Mistyped Characters
  • Summary
• 30. Deduplicating
  • How to Identify Duplicates
  • Causes of Duplicates
    • System Loads
    • Row per Measure
    • Joins
  • How to Handle Duplicates
    • Aggregating: Technique 1
    • Aggregating: Technique 2
    • Pivoting Rows to Columns
  • Summary
• 31. Using Regular Expressions
  • What Are Regular Expressions?
  • How to Use Regexes in Prep
  • REGEXP_EXTRACT() and REGEXP_EXTRACT_NTH()
    • REGEXP_MATCH()
    • REGEXP_REPLACE()
  • Regex Use Cases
    • Replacing Common Mistakes
    • Anonymizing Comments or Feedback
  • Common Regex Commands
  • Summary
• 32. Completing Advanced Joins
  • Multiple Join Conditions
  • Join Conditions Other Than Equals
    • Filtering with a Join
    • Joining by a Range
  • OR Statements
  • Summary
• 33. Creating Level of Detail Calculations
  • What Is Appending?
  • Exploring Appending Through LOD Calculations
    • When to Use an LOD Calculation
    • How to Write an LOD Calculation in Prep Builder
    • What a Level of Detail Calculation Is Doing
      • Step 1: Calculate the categorical sales
      • Step 2: Join the aggregated results back to the original data set
  • Summary
• 34. Doing Analytical Calculations
  • What Is a Table Calculation?
  • Applying Table Calculation Logic in Prep Builder
    • Keywords
    • Analytical Calculations
      • The ranking functions
      • The ROW_NUMBER() function
  • Use Cases
    • Filtering for the Top N
    • Filtering Out a Percentage of Data
  • Summary
• VI. Beyond the Basics
• 35. Breaking Down Complex Data Preparation Challenges
  • The Challenge
  • Where to Begin
  • Logical Steps
  • Making Changes
  • Be Ready to Iterate
  • Summary
• 36. Handling Free Text
  • What Is Free Text?
  • Why Is Free Text Useful?
  • How to Analyze Free Text in Tableau
    • Split the Strings
    • Pivot Columns to Rows
    • Clean Cases and Punctuation
    • Use a Join to Remove Common Words
    • Group the Remaining Values
  • Summary
• 37. Using Smarter Filtering
  • Calculations
    • Boolean Calculations
    • Logical Calculations
    • Regex Calculations
  • Join Ranges
  • Percentage Variance
    • Manual Entry: Level of Detail Calculations
    • Reloaded Data: Join to Previous Output
    • Aggregating the Average Production Cost per Type
    • Joining the Data Sets Together
  • Combining Techniques
  • Summary
• 38. Managing Conversion Rates
  • Challenges of Conversion Rates
  • Applying Conversion Rates in Prep
    • Step 1: Create a Consistent Granularity of Data for the Conversion
    • Step 2: Join the Data Sets Together
    • Step 3: Apply the Conversion Rate
  • Long-Term Strategies for Conversion Rates
    • Managing Frequency
    • Maintaining History Tables
  • Summary
• 39. Scaffolding Your Data
  • What Is Scaffolding?
  • Challenges Addressed by Scaffolding
  • Challenges Created by Scaffolding
  • The Traditional Scaffolding Technique
    • Step 1: Input the Data Sets
    • Step 2: Build the Join Calculations
    • Step 3: Join the Two Data Sets Together
    • Step 4: Filter Out Unnecessary Rows
  • The Newer Scaffolding Technique
    • Step 1: Input the Data Sets
    • Step 2: Join the Data Sets
    • Step 3: Add the Reporting Date
    • Step 4: Remove the Scaffold Value
  • The Result
  • Summary
• 40. Connecting to Programming Scripts
  • When to Use the Script Step in Prep
  • Setting Up Your Computer to Use Scripts in Prep
  • Using a Script Step
  • Summary
• 41. Handling Prep Builder Errors
  • Parameter Errors
  • Blank Profile Panes or Data Panes
    • Changing a Calculation or Removing a Data Field Downstream
    • The Data Source Has Changed
  • Errors Within a Calculated Field
    • Incomplete Calculations
    • Unsupported Functions
  • Summary
• VII. Managing Your Data
• 42. Documenting Your Data Preparation
  • Basic Documentation
    • Folder Structure
    • Filenames
    • Data Sources
    • Output
  • Step Names
  • Clean Step
  • Step Descriptions
  • Color
  • Joins
  • Unions
  • Summary
• 43. Deciding Where to Prepare Your Data
  • Processes to Consider
  • Data Preparation Versus Visual Analytics
    • Data Literacy
    • Organization Size
    • Quality of Technological Hardware
    • History of Data Investment
  • Software Performance
    • Sampling
    • Functionality
    • Documentation
  • Summary
• 44. Managing Data
  • What Is Sensitive Data?
    • Public
    • Confidential
    • Strictly Confidential
    • Restricted
  • Managing Data Based on Sensitivity
  • Production Versus Development Environments
  • Deleting Data
    • When Data Becomes Outdated or Irrelevant
    • When a Customer or Client Leaves
  • Summary
• 45. Storing Your Data
  • Inaccessibility
    • Dont Break the Law
      • Personally identifiable information
      • The right to be forgotten
    • Dont Delete Operational Data
    • Do Grant Access to Data for the Experts
    • Do Document Your Sources
  • Slow/Unresponsive Performance
  • Overwriting Risks
    • Grant Read-Only Access
    • Train Before Publishing
  • So, Where Do You Write That Output?
  • Summary
• 46. Using Identifiers and Keys in Data
  • What Is an Identifier?
  • What Is a Key in a Database?
  • Using Keys and Identifiers in Prep
  • Creating Identifier Data Fields in Prep Builder
  • Summary
• 47. Keeping Your Data Up-to-Date
  • Refreshing Data
  • Full Versus Incremental Refreshes
  • Setting Up Different Types of Refresh
    • Full Refresh
    • Incremental Refresh
  • What to Watch Out for When Refreshing Data Sources
    • Changing Data Values
    • Altering the Structure of Sources
    • New Data, New Input
  • Summary
• 48. Using History Tables
  • Why Are History Tables Required?
  • What to Consider When Creating History Tables
    • Ability to Join to Live Data
    • Relevance of Information
    • Frequency of Updates
    • Level of Granularity
  • Performance
  • Data Regulations
  • An Example History Table
  • Summary
• 49. Evaluating Whether You Need Prep Builder at All
  • A History of Data Preparation in Tableau
  • Where to Try Desktop First
    • Simple Joins
      • When to move simple joins to Prep
    • Unions
      • When to move unions to Prep
    • Single Pivots
      • When to move single pivots to Prep
  • Where to Start with Prep Builder
  • Summary
• 50. Final Thoughts
• Index

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