Recap and best practices for DevOps with Snowflake
1. Recap and best practices for DevOps with Snowflake
Let's recap everything that you learned about DevOps with Snowflake. By this point, you understand that DevOps is a collection of best practices that enable teams to easily collaborate, track their work, and deploy changes quickly and safely. There's more to it than that, but these are the practices that we explored in this module. For our purposes, we focused on how these practices are commonly realized through the use of collaborative tools, automated workflows, source control, and command line tooling. We specifically centered on how these practices can be incorporated for data engineering, and we implemented them directly into our pipeline. You also learned how Snowflake supports each of these aspects. For source control, you know Snowflake's Git integration makes it easy to keep track of changes to your pipeline. You also saw how Snowflake's declarative functionality, create or alter, makes it easy to incrementally iterate on database objects. Paired with source control, this is a powerful declarative approach. For collaboration, we use Snowflake's Git integration with GitHub so that we can build pipelines collaboratively with teammates. For automation, you saw how easy it is to integrate with a third-party tool like GitHub Actions to automate the deployment of changes into Snowflake. And last, for tooling, you use Snowflake CLI to push changes up to GitHub, which triggered deployments into Snowflake. All of this is quite modern. This is a workflow that is common in software engineering, like application development, for example, and is increasingly becoming more common in data engineering. One thing you should keep in mind is that what you learned in this module is pretty cutting-edge stuff with Snowflake. Many of the pieces to get started with DevOps are there, but just know that our teams are continuously adding new features and constantly improving existing ones. There are also many other third-party tools to help augment some of the practices we covered here, like database change management. Great job. Join me in the next module as we go one step further and learn about how to implement observability into your data pipelines.2. Let's practice!
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