Book Image

Snowflake Cookbook

By : Hamid Mahmood Qureshi, Hammad Sharif
5 (1)
Book Image

Snowflake Cookbook

5 (1)
By: Hamid Mahmood Qureshi, Hammad Sharif

Overview of this book

Snowflake is a unique cloud-based data warehousing platform built from scratch to perform data management on the cloud. This book introduces you to Snowflake's unique architecture, which places it at the forefront of cloud data warehouses. You'll explore the compute model available with Snowflake, and find out how Snowflake allows extensive scaling through the virtual warehouses. You will then learn how to configure a virtual warehouse for optimizing cost and performance. Moving on, you'll get to grips with the data ecosystem and discover how Snowflake integrates with other technologies for staging and loading data. As you progress through the chapters, you will leverage Snowflake's capabilities to process a series of SQL statements using tasks to build data pipelines and find out how you can create modern data solutions and pipelines designed to provide high performance and scalability. You will also get to grips with creating role hierarchies, adding custom roles, and setting default roles for users before covering advanced topics such as data sharing, cloning, and performance optimization. By the end of this Snowflake book, you will be well-versed in Snowflake's architecture for building modern analytical solutions and understand best practices for solving commonly faced problems using practical recipes.
Table of Contents (12 chapters)

Identifying query plans and bottlenecks

Through this recipe, you will understand Snowflake's query plans and learn how to identify bottlenecks and inefficiencies by reading through the query plans.

Getting ready

You will need to be connected to your Snowflake instance via the web UI or the SnowSQL client to execute this recipe.

How to do it…

We will be running a sample query using the TPCH sample dataset that is provided with Snowflake. The intent is to run an inefficient query, review its query plan, and identify which steps are using the most compute and contributing most to the overall query execution. The steps are as follows:

  1. We will start by executing a sample query on the TPCH dataset. Now, I am running this query on the X-Small virtual warehouse, so it may take around 15–20 minutes for this query to complete. It will likely complete faster if you are using a larger virtual warehouse. Note that the sample data is present in the SNOWFLAKE_SAMPLE_DATA...