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

The art of SQL Warehouse sizing

Warehouse sizing requires calibrating the cluster size and the scaling range. Simply put, we must configure the speed and concurrency with which the SQL Warehouse will process queries submitted by users.

That said, it is important to understand that speed and concurrency are not entirely independent metrics. For example, if you process your queries faster, then the overall throughput of queries will be higher and the amount of time a query spends in a queued state will be shorter. This will avoid having to scale the concurrency of the SQL Warehouse by increasing the cluster size.

So, let’s start by understanding the mechanics of query routing, queuing, and cluster autoscaling.

Rules for query routing, queuing, and cluster autoscaling

The control processes in the SQL Warehouse follow a very simple decision tree for performing query routing, query queuing, and cluster autoscaling.

The 10-Query Rule

The core rule (at the time of...