Learn how to apply DevOps best practices to Snowflake data pipelines, from source control with Git to declarative database changes with CREATE OR ALTER and automated CI/CD using the Snowflake CLI and GitHub Actions.

Scaling data pipelines to meet modern demands

What this course will cover

DevOps in the world of data engineering

DevOps with Snowflake

What we'll build

Source control in Snowflake with git

Clone your fork, install Snowflake CLI, and configure config.toml

Weather data from Snowflake Marketplace

Set up the data pipeline using Snowflake CLI

Database Change Management (DCM)

Declarative approach with CREATE OR ALTER

Continuous integration and continuous delivery (CI/CD) for data pipelines

Implementing continuous delivery for our data pipeline

Recap and best practices for DevOps with Snowflake

Additional resources on DevOps with Snowflake

Chapter 1 review - Question 1

Chapter 1 review - Question 2

Chapter 1 review - Question 3

Chapter 1 review - Question 4

Chapter 1 review - Question 5

In this final chapter, you'll learn how to make data pipelines observable with Snowflake Trail, using event tables, logging, traces, alerts, and notifications to monitor pipeline health and act on issues quickly.

Observability for data engineering

Foundational concepts of observability

Observability with Snowflake Trail

Event Tables in Snowflake

Logging in Snowflake

Traces in Snowflake

Alerts in Snowflake

Notifications in Snowflake

Observability with third-party tools

Recap and best practices for observability with Snowflake

Conclusion

Chapter 2 review - Question 1

Chapter 2 review - Question 2

Chapter 2 review - Question 3

Chapter 2 review - Question 4

Chapter 2 review - Question 5

Clean up

Additional resources on observability with Snowflake

Observability with Snowflake

Take your Snowflake data pipelines to production with DevOps and observability. In this hands-on course, you'll implement source control with Git, manage database changes declaratively using CREATE OR ALTER, and automate deployments with the Snowflake CLI and GitHub Actions. Then you'll make pipelines observable with Snowflake Trail, using event tables, logging, traces, alerts, and notifications to monitor pipeline health and respond quickly when issues arise. By the end, you'll be able to build, deploy, and maintain reliable pipelines that scale with modern demands.

Introduction to Modern Data Engineering with Snowflake

Build reliable Snowflake pipelines with DevOps and observability: Git, CI/CD, and Snowflake Trail monitoring.


Advanced Data Engineering with Snowflake

Build reliable Snowflake pipelines with DevOps and observability: Git, CI/CD, and Snowflake Trail monitoring.



Chapter 2 review - Question 1

Advanced Data Engineering with Snowflake

esercizio interattivo pratico