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Business Intelligence with Databricks SQL

You're reading from   Business Intelligence with Databricks SQL Concepts, tools, and techniques for scaling business intelligence on the data lakehouse

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Product type Paperback
Published in Sep 2022
Publisher Packt
ISBN-13 9781803235332
Length 348 pages
Edition 1st Edition
Languages
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Author (1):
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Vihag Gupta Vihag Gupta
Author Profile Icon Vihag Gupta
Vihag Gupta
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Databricks SQL on the Lakehouse
2. Chapter 1: Introduction to Databricks FREE CHAPTER 3. Chapter 2: The Databricks Product Suite – A Visual Tour 4. Chapter 3: The Data Catalog 5. Chapter 4: The Security Model 6. Chapter 5: The Workbench 7. Chapter 6: The SQL Warehouses 8. Chapter 7: Using Business Intelligence Tools with Databricks SQL 9. Part 2: Internals of Databricks SQL
10. Chapter 8: The Delta Lake 11. Chapter 9: The Photon Engine 12. Chapter 10: Warehouse on the Lakehouse 13. Part 3: Databricks SQL Commands
14. Chapter 11: SQL Commands – Part 1 15. Chapter 12: SQL Commands – Part 2 16. Part 4: TPC-DS, Experiments, and Frequently Asked Questions
17. Chapter 13: Playing with the TPC-DS Dataset 18. Chapter 14: Ask Me Anything 19. Index 20. Other Books You May Enjoy

The Machine Learning persona view

In this section, we will cover the Machine Learning persona. To switch to the Machine Learning persona, click on the Machine Learning option in the persona switching menu, as shown in the following screenshot:

Figure 2.16 – Switching to the Machine Learning persona

Switching to the Machine Learning persona will bring up the landing page for Machine Learning. The following screenshot shows a section of the landing page:

Figure 2.17 – The Machine Learning persona landing page

The first thing that should stand out is the familiarity of experience. The left-hand sidebar brings up familiar icons, such as the following:

  • Create : Like the SQL persona, the Create button provides easy navigation to creating artifacts relevant to the Machine Learning persona. These artifacts can be divided into two groups:
    • Machine Learning: Databricks AutoML Experiments, MLflow Experiments, and MLflow Models...
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