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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

Fundamentals of the Delta Lake storage format

In this section, we will learn about the Delta Lake storage format and its core objectives. To do so, we will first detour into the realm of data engineering, which will provide the context for the core objectives. Finally, we will see how the Delta Lake format achieves the core objectives and simplifies the data engineering process.

Delta Lake (https://delta.io/) is an open source storage format that enables an organization’s data engineering teams to build the Lakehouse (see Chapter 1, Introduction to Databricks). Delta Lake aims to bring the following data warehousing characteristics to your data lake:

  • Reliability
  • Simplicity
  • Performance
  • Governance

To understand why this is a big deal, we must understand how data engineering on data lakes is currently done, how it affects BI users, and why these characteristics are the holy grail for data engineering teams.

Data engineering before Delta Lake

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