Data modeling with tableau a practical guide to building data models using tableau prep and tableau desktop
Save time analyzing volumes of data using best practices to extract, model, and create insights from your data Key Features Master best practices in data modeling with Tableau Prep Builder and Tableau Desktop Apply Tableau Server and Cloud to create and extend data models Build organizational data m...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
London, England :
Packt Publishing
[2022]
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| Edición: | 1st ed |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009714840006719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright and Credit
- Dedicated
- Contributors
- Table of Contents
- Preface
- Part 1: Data Modeling on the Tableau Platform
- Chapter 1: Introducing Data Modeling in Tableau
- Technical requirements
- What happens when you connect to data in Tableau Desktop?
- The ideal data format for Tableau - table format
- Shaping data for Tableau
- Connecting multiple tables to add new columns
- Summary
- Chapter 2: Licensing Considerations and Types of Data Models
- Tableau roles - Viewer, Explorer, and Creator
- Tableau Data Management
- Tableau virtual connections
- Tableau published data sources
- Working with published data sources on Tableau Server and Tableau Cloud
- Tableau embedded data sources
- Live versus extracted data
- The Tableau Hyper engine
- Summary
- Part 2: Tableau Prep Builder for Data Modeling
- Chapter 3: Data Preparation with Tableau Prep Builder
- Using Tableau Prep Builder to connect to data
- Profiling, cleaning, and grouping data
- Row-level calculations and hiding and removing fields
- Recommendations and changes
- Summary
- Chapter 4: Data Modeling Functions with Tableau Prep Builder
- Adding rows to our data model with unions and wildcard unions
- Adding new columns by joining data
- Dealing with data when columns contain values and not distinct fields
- Aggregating data
- Summary
- Chapter 5: Advanced Modeling Functions in Tableau Prep Builder
- Adding new rows
- Pivoting rows to columns
- Inserting data science models
- Summary
- Chapter 6: Data Output from Tableau Prep Builder
- Outputting our data models to files
- Outputting our data models to published data sources
- Outputting our data models to database tables
- Summary
- Part 3: Tableau Desktop for Data Modeling
- Chapter 7: Connecting to Data in Tableau Desktop
- Technical requirements.
- Connecting to files in Tableau Desktop
- Getting data from Microsoft Excel files
- Getting data from text (or delimited) files
- Importing geospatial file types to allow for visual analysis with maps
- Creating data models from statistical files
- Creating data models from JSON files
- Getting data from tables in PDF files
- Dealing with preformatted reporting files with data interpreter and pivoting columns to rows
- Connecting to servers through installed connectors
- Connecting to servers through other connectors
- Additional connectors
- Web data connectors
- Connecting to databases without a listed connection
- Connecting to the Tableau data server
- Summary
- Chapter 8: Building Data Models Using Relationships
- Technical requirements
- Using relationships to combine tables at the logical layer
- Many use cases with a single data model
- Ability to handle tables at different levels of aggregation
- Understanding the differences between relationships and joins
- Setting performance options for relationships
- Creating manual and wildcard unions in Tableau Desktop to add additional rows of data
- Manual union
- Summary
- Chapter 9: Building Data Models at the Physical Level
- Technical requirements
- Opening relationships to join at the physical layer through database joins
- Single use case and using the join for a filter
- Geospatial join type to drive map-based analysis
- Using joins to create a data model with row-level security
- Understanding custom SQL - when to use it and the pitfalls of using it
- Summary
- Chapter 10: Sharing and Extending Tableau Data Models
- Technical requirements
- Understanding live connections and extracts - scenarios for using each
- Creating extracts with the Tableau Hyper engine
- Understanding extracts and data source filters.
- Understanding the implications of an embedded data source versus a published data source
- Creating a published data source from the web interface of Tableau Server or Cloud
- Extending the Tableau data model with calculations, folders, hierarchies, grouping, and descriptions
- Summary
- Part 4: Data Modeling with Tableau Server and Online
- Chapter 11: Securing Data
- Technical requirements
- Adding users and groups to Tableau Server and Cloud
- Using Tableau projects to manage data model security
- Adding user-based security using a user filter
- Adding user-based security inside a published data source using an entitlements table
- Using Tableau virtual connections to manage access and security
- Leveraging database security features for both row and column-level security
- Summary
- Chapter 12: Data Modeling Considerations for Ask Data and Explain Data
- Technical requirements
- Visual analytics through natural language search with Ask Data
- Creating a lens for Ask Data, including field exclusions, renaming, and creating aliases
- Uncovering outliers in your data with Explain Data
- Curating data sources for Explain Data by telling the model which columns to use and ignore
- Summary
- Chapter 13: Data Management with Tableau Prep Conductor
- Technical requirements
- Scheduling Tableau Prep flows from Tableau Prep Conductor
- Data catalog, data lineage, data quality warnings, and certified data sources
- Summary
- Chapter 14: Scheduling Extract Refreshes
- Technical requirements
- How to set up and run schedules
- Using schedules with subscriptions
- Tableau Bridge - what it is and when to use it
- Summary
- Chapter 15: Data Modeling Strategies by Audience and Use Case
- When to use Tableau Prep Builder versus Tableau Desktop for creating our data models.
- Use case 1 - finance user with quarterly financial reporting
- Use case 2 - sales performance management dashboards
- Use case 3 - information systems analytics of internal employee intranet site visits
- Use case 4 - marketing analytics of social media campaigns
- Summary
- Index
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