Book Image

Business Intelligence with Databricks SQL

By : Vihag Gupta
Book Image

Business Intelligence with Databricks SQL

By: Vihag Gupta

Overview of this book

In this new era of data platform system design, data lakes and data warehouses are giving way to the lakehouse – a new type of data platform system that aims to unify all data analytics into a single platform. Databricks, with its Databricks SQL product suite, is the hottest lakehouse platform out there, harnessing the power of Apache Spark™, Delta Lake, and other innovations to enable data warehousing capabilities on the lakehouse with data lake economics. This book is a comprehensive hands-on guide that helps you explore all the advanced features, use cases, and technology components of Databricks SQL. You’ll start with the lakehouse architecture fundamentals and understand how Databricks SQL fits into it. The book then shows you how to use the platform, from exploring data, executing queries, building reports, and using dashboards through to learning the administrative aspects of the lakehouse – data security, governance, and management of the computational power of the lakehouse. You’ll also delve into the core technology enablers of Databricks SQL – Delta Lake and Photon. Finally, you’ll get hands-on with advanced SQL commands for ingesting data and maintaining the lakehouse. By the end of this book, you’ll have mastered Databricks SQL and be able to deploy and deliver fast, scalable business intelligence on the lakehouse.
Table of Contents (21 chapters)
1
Part 1: Databricks SQL on the Lakehouse
9
Part 2: Internals of Databricks SQL
13
Part 3: Databricks SQL Commands
16
Part 4: TPC-DS, Experiments, and Frequently Asked Questions

Summary

In this chapter, we took a visual tour of the Databricks Lakehouse platform. We focused on the lakehouse platform primarily from the perspective of a business intelligence user – that is, the SQL persona. We familiarized ourselves with the various features and toolsets that the Databricks SQL product provides to enable the daily activities of users.

We also briefly examined the Data Science & Engineering and Machine Learning persona views of the Databricks Lakehouse platform, which we will not revisit as they are not the focus of this book.

Finally, while looking at the various features and toolsets, we learned how they map to the various layers of the Lakehouse architecture and how the same data is leveraged for all users of the data in the Lakehouse.

In the next chapter, we will start our deep dive into the features and toolsets in the Databricks SQL product, starting with the data catalog.