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.

Join this hands-on lab to learn:

  • Key Snowflake and dbt concepts such as base views, write, layer, run, and document
  • Creating data models with dbt
  • Running reliable and high-performance data transformation using Snowflake
  • Dmytro Yaroshenko

    Field CTO Office, Snowflake