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)

Ordered analytics over window frames

The recipe will provide you with insight into Snowflake's ability to run ordered or simple analytics over subsets of rows. Such analytics are typically used in marketing analytics applications, where moving average or cumulative functions are applied to data to identify trends. These capabilities help data scientists wrangle large datasets.

Getting ready

Note that this recipe's steps can be run either in the Snowflake WebUI or the SnowSQL command-line client. We shall be generating data that we intend to use in this recipe. The dataset will have three columns: customer_id, deposit_dt, and deposit. This data will capture deposits that have been made by a customer on a particular date.

How to do it…

Let's start by generating some sample data. We shall create a view with the logic to generate data:

  1. The following query will be used to generate base data that will be used to implement a view. We will be using...