Modern businesses need a data strategy built on a platform that can support agility, growth, and operational efficiency. 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-service 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 virtual hands-on lab, you will follow a step-by-step guide to subscribe securely to live trading and foreign exchange data sets from the Snowflake Data Marketplace. You’ll also build a scalable data pipeline using dbt on the subscribed data sets to calculate profit and loss for financial reporting and insight.
- 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