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Tableau 2019.x Cookbook

You're reading from   Tableau 2019.x Cookbook Over 115 recipes to build end-to-end analytical solutions using Tableau

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789533385
Length 670 pages
Edition 1st Edition
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Authors (6):
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Tania Lincoln Tania Lincoln
Author Profile Icon Tania Lincoln
Tania Lincoln
Slaven Bogdanovic Slaven Bogdanovic
Author Profile Icon Slaven Bogdanovic
Slaven Bogdanovic
Teodora Matic Teodora Matic
Author Profile Icon Teodora Matic
Teodora Matic
Rintaro Sugimura Rintaro Sugimura
Author Profile Icon Rintaro Sugimura
Rintaro Sugimura
Dmitry Anoshin Dmitry Anoshin
Author Profile Icon Dmitry Anoshin
Dmitry Anoshin
Dmitrii Shirokov Dmitrii Shirokov
Author Profile Icon Dmitrii Shirokov
Dmitrii Shirokov
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Table of Contents (18) Chapters Close

Preface 1. Getting Started with Tableau Software 2. Data Manipulation FREE CHAPTER 3. Tableau Extracts 4. Tableau Desktop Advanced Calculations 5. Tableau Desktop Advanced Filtering 6. Building Dashboards 7. Telling a Story with Tableau 8. Tableau Visualization 9. Tableau Advanced Visualization 10. Tableau for Big Data 11. Forecasting with Tableau 12. Advanced Analytics with Tableau 13. Deploy Tableau Server 14. Tableau Troubleshooting 15. Preparing Data for Analysis with Tableau Prep 16. ETL Best Practices for Tableau 17. Other Books You May Enjoy

Accessing semi–structured data

In the modern world, often we can meet unstructured data that is generating by machines, apps, sensors, and so on. There are the following two main attributes of semi-structured data that differ it from structure data:

  • Semi-structured data can contain n-level hierarchies of nested information
  • Structured data always needs a defined schema before loading it. Semi-structured data doesn't need this, so as a result we can create the schema on the fly

Despite the fact that Tableau supports direct connection to the JSON format, we still have the same issue with big data, when we need more compute resource than Tableau allows us to use and also, we can collect data types such as Avro, ORC, Parquet, and XML.

Usually, we should parse unstructured data and write into the table. But not with Snowflake; it has a special data type VARIANT that allows...

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