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Data Democratization with Domo

You're reading from   Data Democratization with Domo Bring together every component of your business to make better data-driven decisions using Domo

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Product type Paperback
Published in Jun 2022
Publisher Packt
ISBN-13 9781800568426
Length 576 pages
Edition 1st Edition
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Author (1):
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Jeff Burtenshaw Jeff Burtenshaw
Author Profile Icon Jeff Burtenshaw
Jeff Burtenshaw
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Table of Contents (26) Chapters Close

Preface 1. Section 1: Data Pipelines
2. Chapter 1: Domo Ecosystem Overview FREE CHAPTER 3. Chapter 2: Importing Data 4. Chapter 3: Storing Data 5. Chapter 4: Sculpting Data 6. Chapter 5: Sculpting Data In-Memory 7. Section 2: Presenting the Message
8. Chapter 6: Creating Dashboards 9. Chapter 7: Working with Drill Pathways 10. Chapter 8: Interacting with Dashboards 11. Chapter 9: Interacting with Cards 12. Section 3: Communicating to Win
13. Chapter 10: Telling Relevant Stories 14. Chapter 11: Distributing Stories 15. Chapter 12: Alerting 16. Chapter 13: Buzzing 17. Section 4: Extending
18. Chapter 14: Extending Domo with Domo Apps 19. Chapter 15: Using Domo APIs in Python 20. Chapter 16: Using Domo Machine Learning 21. Section 5: Governing
22. Chapter 17: Securing Assets 23. Chapter 18: Organizing the Team 24. Chapter 19: Establishing Standard Procedures 25. Other Books You May Enjoy

Summary

In this chapter, we reviewed the in-memory data sculpting tools that are available in Domo and how to decide which tool to use. Data Views are the most flexible and powerful but also the more complex to use. Then, there are Data Blends, which allow you to perform VLOOKUPs at scale. Additionally, Beast Modes are a powerful way to extend existing datasets with calculated fields. All of these tools work in the Adrenaline cache, so they are extremely fast. Finally, for those working with datasets with billions of rows, we made you aware of the Adrenaline DataFlows tool for handling data transformation at a mind-boggling scale. Having this in-memory data sculpting power is a key feature that democratizes the process so that an end user doesn't have to wait for specialized resources to assist.

In the next chapter, we will move up from the data transformation layer to the data visualization layer features of the platform, including dashboards.

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