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 build a set of data analytics pipelines for retail data leveraging dbt and Snowflake, making use of best practices like data quality tests and code promotion between environments. This lab will cover how to:

• Build scalable data transformation pipelines using dbt & Snowflake
• Establish data trust with stakeholders by incorporating key dbt testing capabilities
• Scale Snowflake compute capabilities with the dbt workflow
• Build lightweight charts and visualisations in Snowflake

In Partnership with:-