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)

Chapter 4: Building Data Pipelines in Snowflake

Snowflake, like other data platforms, offers tools and abstractions to developers/users to build data pipelines to enable data processing and analytics. But being a cloud database, it has different ways of handling data pipelines. In a typical data pipeline, there are ways to execute a piece of code, sequence pieces of code to execute one after the other, and create dependencies within the pipeline and on the environment. Snowflake structures pipelines using the notions of tasks and streams. A pipeline allows developers to create a sequence of data processes that are represented by tasks. A task represents a data process that can be logically atomic. The other concept of a stream allows data processing applications to be intelligent, triggering data processing based on a change happening in the data landscape.

This chapter deals with setting up pipelines using tasks and streams and applying different techniques for transforming data...