Throughout the ingestion, transformation, and delivery processes of data engineering, a large amount of data engineers’ time is often spent on manual engineering tasks. With Snowflake and dbt, that’s no longer the case. Together, Snowflake and dbt automate mundane tasks to handle data engineering workloads with simplicity and elasticity, accelerating the time to value for your data while opening up opportunities for self-serve data engineering. This enables you to focus on data without worrying about tasks such as capacity planning, performance tuning, resource allocation, testing, change management, documentation, CI/CD, and so on.
In this hands-on lab session, you will follow our instructor with a step-by-step guide using SQL to transform data with the instant scalability of Snowflake and to easily apply engineering principles with dbt.
You will learn about:
- Key Snowflake and dbt concepts such as base views, write, layer, run and document
- Creating data models with dbt
- Testing, deployment and materialization
- Running reliable and high-performance transformation using Snowflake
- Bonus: change management
Field CTO Office, Snowflake