Allchin C. / Оллчин К. - Tableau Prep: Up & Running / Tableau Prep: настраиваем и запускаем [2020, PDF, ENG]

Примеры страниц

Оглавление

Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xix
1. Why Self-Service Data Prep?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
A Short History of Self-Service Data Visualization 1
Accessing the “Right Data” 2
The Self-Service Data Preparation Opportunity 3
Tableau Prep Up and Running 4
Summary 4
Part I. Getting Started
2. Getting Started with Tableau Prep Builder. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Where to Get Tableau Prep Builder 6
How to Get a License for Prep Builder 7
The Tableau Prep Builder Screen 9
Basic Steps of Data Preparation 11
Input Step 11
Clean Step 12
Output Step 13
Saving a Flow 13
Summary 15
3. Planning Your Prep. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Stage 1: Know Your Data 17
Stage 2: Identify the Desired State 18
Stage 3: Determine the Required Transitions from KYD to the Desired State 20
Stage 4: Build the Workflow 22
Summary 24
4. Shaping Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
What to Look for in Incoming Data Sets 25
What Shape Is Best for Analysis in Tableau? 26
Changing Data Set Structures in Prep Builder 28
Pivot 28
Aggregate 29
Join 30
Union 31
Applying Restructuring Techniques to the Ice Cream Example 32
Step 1: Pivot Columns to Rows 32
Step 2: Pivot Rows to Columns 32
Summary 33
5. Connecting to Data in Files. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Files Upon Files Upon Files 34
Spreadsheets 35
Other File Types 35
Where to Find Your Data Files 36
How to Connect to Files in Prep 37
Considerations for Saving Flows with File Inputs 39
Summary 39
6. Connecting to a Database. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
What Is a Database? 40
How to Connect to a Database Within Prep Builder 42
When to Avoid Connecting to a Database 45
Summary 46
Part II. Data Types
7. Dealing with Numbers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
What Do We Mean by Numbers? 49
Types of Numbers 49
Category or Measure? 49
Aggregation 50
Formatting Numbers 50
Functions for Mastering Numerical Data 51
Summary 53
8. Dealing with Dates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
Why Are Dates Important? 54
Parts of a Date 54
Date Lookup Tables 55
Epoch Dates 56
Excel Serial Number 57
Entering Dates 57
The makedate() Function 58
The dateparse() Function 58
Summary 59
9. Dealing with String Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
What Do We Mean by Strings? 60
How String Data Is Different 61
Character Order 61
Formatting Considerations 61
Common Functions for Preparing String Data 63
Grouping and Replace Options for Working with
String Data 65
Summary 65
10. Dealing with Boolean Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
What Is Boolean Data? 66
Why Is It So Useful in Data Analysis? 66
Functions Featuring Boolean Logic 68
Summary 74
Part III. The Shape of Data
11. Profiling Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
What Is a Profile? 76
Why Visualizing the Data Set Is Important 77
Anscombe’s Quartet 77
Visualizations Versus Data Tables 79
How Prep Builder Profiles Data 80
Generating Histograms and Mini-Histograms 80
Selecting Summary Versus Detail Views 82
Highlighting Values 83
Viewing Dimension Counts 84
Sorting 85
Summary 85
12. Sampling Data Sets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
One Simple Rule: Use It All If Possible 86
Sampling to Work Around Technical Limitations 86
Volume of Data 87
Velocity of Data 87
Other Reasons for Sampling 88
Reduce Build Times 88
Determine What You Need 88
Sampling Techniques 89
Fixed Number of Rows 89
Random Sample 90
When Not to Sample 91
Summary 92
13. Pivoting Columns to Rows. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
When to Pivot in Tableau Prep Builder 93
How to Pivot Columns to Rows 95
Summary 99
14. Pivoting Rows to Columns. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
When to Use a Rows-to-Columns Pivot 100
How to Pivot Rows to Columns 101
Summary 103
15. Aggregating in Prep Builder. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
Comparing Calculations in Prep Builder and Desktop 104
Which Calculations in Prep Builder Differ? 105
Adding the Aggregate Step 109
Where’s the Rest of My Data? 113
Level of Detail Calculation Option 114
Summary 114
16. Joining Data Sets Together. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
How to Join Data Sets in Prep Builder 115
Join Logic and Terminology 119
Types of Join in Prep Builder 120
When to Use Each Join Type 125
Summary 126
17. Unioning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
What Is a Union? 127
What If the Data Structure Isn’t Identical? 128
When to Union Data 130
Monthly Data Sets 130
Data Sets from Web Sources 131
Company Mergers 133
Multiple Tables and Wildcard Unions 133
Summary 135
18. Calculations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
What Do Calculations Do in Data Preparation? 136
Creating a Calculated Field 137
Fundamentals of Calculations 139
The Reference List 139
Syntax 140
Description 141
Example 141
Building the Calculation 141
When Calculations Go Well 141
When Calculations Go Poorly 143
Editing Calculated Fields 144
Recommendations 144
Types of Calculations 145
Numerical Calculations 146
String Calculations 146
Date Calculations 146
Conditional Calculations with a Boolean Output 146
Logical Calculations 146
Type Conversions 146
Level of Detail and Ranking Calculations 147
Summary 148
Part IV. Output
19. Choosing an Output. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
Types of Output 150
Publish to Files 151
Publish to Tableau Server 152
When to Output Data in Prep Builder 152
Outputting Data in the Output Step 152
Previewing Output Data in Desktop 155
Other Considerations for Output Data 157
Summary 157
20. Outputting to a Database. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
When to Write to a Database 158
Clean Data 158
Simplified Joins 159
Staging and Reference Tables 159
Setup for Writing to a Database 159
What to Watch Out For 163
Summary 163
21. Getting Started with Tableau
Prep Conductor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
When to Use Prep Conductor 164
How to Get Prep Conductor 165
Loading a Flow to Prep Conductor 165
Other Benefits of Using Prep Conductor 172
Summary 173
Part V. Cleaning Data
22. Creating Additional Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
When Not to Create Data 175
Dynamic Calculations in Desktop 175
Duplicate Records from Joins 177
Creating Additional Columns 177
Using Calculations 178
Pivoting Rows to Columns 178
Joining Data Sets 179
Creating Additional Rows 180
Pivoting Columns to Rows 180
Unioning Data Sets 180
Scaffolding Data Sets 181
Joining Data Sets 181
Summary 181
23. Filtering. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
What Is a Filter? 182
Different Types of Filters 183
Selection 183
Calculation 186
Wildcard 188
Null Values 188
When to Filter Out Columns 189
When to Filter Out Rows 189
Summary 190
24. Removing Data During Input. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
Changing Your Data Set Before Loading It 191
Slow Performance, Slow Build, Slow Output 192
Removing Columns 194
Removing Records 196
Summary 198
25. Splitting Data Fields. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
Basic Splits 200
Advanced Splits: When Automatic Splits Don’t Work as Intended 202
When Not to Split Data 204
Address Data 204
No Clear Delimiter 205
Summary 205
26. Cleaning by Grouping Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
What Does Grouping Mean? 206
Why Use Grouping 207
Improving Accuracy 207
Navigating the Data Hierarchy 207
Smoothing Reorganizations 208
Grouping Techniques 209
Manual 209
Calculations 212
Built-in Functionality 213
Summary 217
27. Dealing with Nulls. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218
What Is a Null? 218
When Is a Null OK? 219
How to Remove or Replace a Null 221
ISNULL() 221
ZN() 222
Merge 223
Summary 225
28. Using Data Roles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226
How to Use Data Roles 227
Custom Data Roles 229
Summary 233
29. Dealing with Unwanted Characters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234
What Is an Unwanted Character? 234
Issues Caused by Unwanted Characters 235
Removing Unwanted Characters 237
Strings with Mistyped Characters 238
Numbers with Unwanted Characters 239
Dates with Mistyped Characters 240
Summary 241
30. Deduplicating. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242
How to Identify Duplicates 242
Causes of Duplicates 243
System Loads 243
Row per Measure 244
Joins 245
How to Handle Duplicates 246
Aggregating: Technique 1 246
Aggregating: Technique 2 248
Pivoting Rows to Columns 250
Summary 251
31. Using Regular Expressions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252
What Are Regular Expressions? 252
How to Use Regexes in Prep 252
REGEXP_EXTRACT() and REGEXP_EXTRACT_NTH() 253
REGEXP_MATCH() 253
REGEXP_REPLACE() 254
Regex Use Cases 254
Replacing Common Mistakes 254
Anonymizing Comments or Feedback 255
Common Regex Commands 256
Summary 256
32. Completing Advanced Joins. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257
Multiple Join Conditions 257
Join Conditions Other Than Equals 260
Filtering with a Join 261
Joining by a Range 262
OR Statements 265
Summary 266
33. Creating Level of Detail Calculations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267
What Is Appending? 267
Exploring Appending Through LOD Calculations 267
When to Use an LOD Calculation 267
How to Write an LOD Calculation in Prep Builder 269
What a Level of Detail Calculation Is Doing 273
Summary 275
34. Doing Analytical Calculations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276
What Is a Table Calculation? 276
Applying Table Calculation Logic in Prep Builder 279
Keywords 280
Analytical Calculations 280
Use Cases 284
Filtering for the Top N 284
Filtering Out a Percentage of Data 285
Summary 286
Part VI. Beyond the Basics
35. Breaking Down Complex Data Preparation Challenges. . . . . . . . . . . . . . . . . . . . . . . . . 288
The Challenge 288
Where to Begin 289
Logical Steps 291
Making Changes 294
Be Ready to Iterate 295
Summary 297
36. Handling Free Text. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298
What Is Free Text? 298
Why Is Free Text Useful? 298
How to Analyze Free Text in Tableau 299
Split the Strings 300
Pivot Columns to Rows 301
Clean Cases and Punctuation 303
Use a Join to Remove Common Words 303
Group the Remaining Values 305
Summary 307
37. Using Smarter Filtering. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308
Calculations 308
Boolean Calculations 308
Logical Calculations 309
Regex Calculations 310
Join Ranges 312
Percentage Variance 312
Manual Entry: Level of Detail Calculations 312
Reloaded Data: Join to Previous Output 315
Aggregating the Average Production Cost per Type 316
Joining the Data Sets Together 316
Combining Techniques 317
Summary 318
38. Managing Conversion Rates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319
Challenges of Conversion Rates 319
Applying Conversion Rates in Prep 320
Step 1: Create a Consistent Granularity of Data for the Conversion 320
Step 2: Join the Data Sets Together 321
Step 3: Apply the Conversion Rate 322
Long-Term Strategies for Conversion Rates 322
Managing Frequency 322
Maintaining History Tables 322
Summary 323
39. Scaffolding Your Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324
What Is Scaffolding? 324
Challenges Addressed by Scaffolding 327
Challenges Created by Scaffolding 327
The Traditional Scaffolding Technique 328
Step 1: Input the Data Sets 329
Step 2: Build the Join Calculations 329
Step 3: Join the Two Data Sets Together 331
Step 4: Filter Out Unnecessary Rows 332
The Newer Scaffolding Technique 334
Step 1: Input the Data Sets 335
Step 2: Join the Data Sets 335
Step 3: Add the Reporting Date 336
Step 4: Remove the Scaffold Value 336
The Result 337
Summary 337
40. Connecting to Programming Scripts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338
When to Use the Script Step in Prep 338
Setting Up Your Computer to Use Scripts in Prep 339
Using a Script Step 343
Summary 345
41. Handling Prep Builder Errors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346
Parameter Errors 346
Blank Profile Panes or Data Panes 347
Changing a Calculation or Removing a Data Field Downstream 347
The Data Source Has Changed 348
Errors Within a Calculated Field 349
Incomplete Calculations 349
Unsupported Functions 351
Summary 351
Part VII. Managing Your Data
42. Documenting Your Data Preparation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353
Basic Documentation 353
Folder Structure 353
Filenames 354
Data Sources 354
Output 354
Step Names 355
Clean Step 355
Step Descriptions 356
Color 356
Joins 357
Unions 357
Summary 359
43. Deciding Where to Prepare Your Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360
Processes to Consider 360
Data Preparation Versus Visual Analytics 361
Data Literacy 361
Organization Size 361
Quality of Technological Hardware 361
History of Data Investment 362
Software Performance 362
Sampling 362
Functionality 363
Documentation 364
Summary 365
44. Managing Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366
What Is Sensitive Data? 366
Public 367
Confidential 367
Strictly Confidential 367
Restricted 367
Managing Data Based on Sensitivity 368
Production Versus Development Environments 368
Deleting Data 369
When Data Becomes Outdated or Irrelevant 369
When a Customer or Client Leaves 369
Summary 369
45. Storing Your Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 370
Inaccessibility 370
Don’t Break the Law 371
Don’t Delete Operational Data 371
Do Grant Access to Data for the Experts 371
Do Document Your Sources 372
Slow/Unresponsive Performance 372
Overwriting Risks 372
Grant Read-Only Access 373
Train Before Publishing 373
So, Where Do You Write That Output? 373
Summary 374
46. Using Identifiers and Keys in Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375
What Is an Identifier? 375
What Is a Key in a Database? 376
Using Keys and Identifiers in Prep 377
Creating Identifier Data Fields in Prep Builder 379
Summary 382
47. Keeping Your Data Up-to-Date. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383
Refreshing Data 383
Full Versus Incremental Refreshes 384
Setting Up Different Types of Refresh 384
Full Refresh 384
Incremental Refresh 385
What to Watch Out for When Refreshing Data Sources 388
Changing Data Values 388
Altering the Structure of Sources 388
New Data, New Input 388
Summary 389
48. Using History Tables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390
Why Are History Tables Required? 390
What to Consider When Creating History Tables 391
Ability to Join to Live Data 391
Relevance of Information 391
Frequency of Updates 392
Level of Granularity 392
Performance 392
Data Regulations 393
An Example History Table 393
Summary 397
49. Evaluating Whether You Need Prep Builder at All. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398
A History of Data Preparation in Tableau 398
Where to Try Desktop First 399
Simple Joins 399
Unions 400
Single Pivots 402
Where to Start with Prep Builder 403
Summary 404
50. Final Thoughts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405
Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407