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

Built-in performance-boosting features of Delta Lake

Delta Lake provides built-in performance boosters that complement the data layout strategies that we discussed in the Optimizing the data layout section. If there is a well-working data layout strategy in place, performance is accelerated further. If the data layout strategy is lacking or limited due to a wide variety of query-filtering patterns on the data, then the boosters make sure that performance is still improved by reducing unnecessary I/O. Let’s learn about these performance boosters.

Automatic statistics collection

The first, and arguably the most important, performance booster is automatic statistics collection (stats collection for short), which enables a process called data skipping. Stats collection is an automatic process on Delta Lake. For every data file written, the stats collection process computes the minimum and maximum values for the columns present in the file.

By default, stats collection...