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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

An overview of Databricks, the company

Databricks was founded in 2013 by seven researchers at the University of California, Berkeley.

This was the time when the world was learning how the Meta, Amazon, Netflix, Google, and Apple (MANGA) companies had built their success by scaling up their use of AI techniques in all aspects of their operations. Of course, they could do this because they invested heavily in talent and infrastructure to build their data and AI systems. Databricks was founded with the mission to enable everyone else to do the same – use data and AI in service of their business, irrespective of their size, scale, or technological prowess.

The mission was to democratize AI. What started as a simple platform, leveraging the open source technologies that the co-founders of Databricks had created, has now evolved into the lakehouse platform, which unifies data, analytics, and AI in one place.

As an interesting side note, and my opinion: To this date, I meet people and organizations that equate Databricks with Apache Spark. This is not correct. The platform indeed debuted with a cloud service for running Apache Spark. However, it is important to understand that Apache Spark was the enabling technology for the big data processing platform. It was not the product. The product is a simple platform that enables the democratization of data and AI.

Databricks is a strong proponent of the open source community. A lot of popular open source projects trace their roots to Databricks, including MLflow, Koalas, and Delta Lake. The profile of these innovations demonstrates the commitment to Databricks’s mission statement of democratizing data and AI. MLflow is an open source technology that enables machine learning (ML) operations or MLOps. Delta Lake is the key innovation that brings reliability, governance, and simplification to data engineering and business intelligence operations on the data lake. It is the key to building the lakehouse on top of cloud storage systems such as Amazon Web Service’s Simple Storage Service (S3), Microsoft Azure’s Azure Data Lake Storage (ADLS), and Google Cloud Storage (GCS), as well as on-premises HDFS systems.

Within the Databricks platform, these open source technologies are firmed up for enterprise readiness. They are blended with platform innovations for various data personas such as data engineers, data scientists, and data analysts. This means that MLflow within the Databricks Lakehouse platform powers enterprise-grade MLOps. Delta Lake within the Databricks Lakehouse platform powers enterprise-grade data engineering and data governance. With the Databricks SQL product, the Databricks Lakehouse platform can power all the business intelligence needs for the enterprise as well!

Technologies and Trademarks

Throughout this book we will refer to trademarked technologies and products. Some notable examples are Apache Spark™, Hive™, Delta Lake™, Power BI™, Tableau™ and others that are inadvertently mentioned.

All such trademarks are implied whenever we mention them in the book. For the sake of brevity and readability, I will omit the use of the ™ symbol in the rest of the book.

You have been reading a chapter from
Business Intelligence with Databricks SQL
Published in: Sep 2022
Publisher: Packt
ISBN-13: 9781803235332
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