DevOps with Snowflake
1. DevOps with Snowflake
Continuously evolving data pipelines in a fast, yet safe and reliable way, is at the heart of what DevOps for data engineering can help do. In this exercise, we'll cover how Snowflake supports the following DevOps practices, specifically around source control, collaboration, declarative management of code, automation, and tooling. Let's walk through the key features within Snowflake that support each of these best practices. First, Snowflake's Git integration. This feature means you can use Git for source control along with the source control platform, like GitHub, for example, and then connect your Snowflake account directly to the repository containing the code. This means your team can collaborate on your pipeline together, use source control for your code, and run files in Snowflake when needed. The next feature is a new SQL command that goes hand-in-hand with source control, create or alter. The create or alter command allows you to declaratively manage Snowflake objects, like tables, for example. It lets you apply incremental updates to database objects using a declarative, idempotent method. This means you can define your objects once and update them as needed when necessary. This feature becomes incredibly powerful when paired with source control, because you can simply update an object's definition, and then use source control to track all changes ever made to that object. We'll dive into a lot more detail on this topic in an upcoming exercise. These next two things will go hand-in-hand, and we'll round out what we'll cover in this course as it relates to DevOps with Snowflake for data engineering. I'm referring specifically to Snowflake's command-line interface, Snowflake CLI, and GitHub Actions. The Snowflake CLI is a powerful command-line interface for doing all sorts of things with Snowflake, from creating applications that run on Snowflake, creating Snowpark projects, managing Snowpark container services, and much more. In the context of DevOps for our data pipelines, you can use Snowflake CLI for a couple of important things. First, to communicate directly with the Git repository you're using for your data pipeline code. Second, to integrate with automation platforms like GitHub Actions to enable continuous deployment of changes to your pipelines. To recap, these are the features and platforms we'll use in this module to implement DevOps practices for data engineering with Snowflake. Snowflake's Git integration, with GitHub as a source control platform, Snowflake's create or alter SQL command, Snowflake CLI, and GitHub Actions. With these features, we can start implementing DevOps practices for robust and reliable data pipelines. Join me in the next video to learn about exactly what we'll build in the remainder of the module.2. Let's practice!
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