This talk will introduce dbt and demonstrate how to leverage Python to unlock its full potential. Attendees will learn best practices for working with dbt, how to integrate it with other tools in their data stack, and how to use Python packages like fal to perform complex data analysis. With real-world examples and use cases, this talk will equip attendees with the tools to build a modern, scalable, and maintainable data infrastructure.
Data modeling, transformation, and analysis are integral parts of data pipelines. However, managing and maintaining data infrastructure can be a daunting task. dbt (data build tool) is a powerful open source package that helps data teams build modular, maintainable, and scalable data transformations.
In this talk, we will introduce dbt to the Python community and demonstrate its full potential. We will go over best practices for data modeling and transformation, and how to integrate dbt into your existing data stack. We will also show how to use Python packages such as fal to interact with dbt and perform complex data analysis.
Attendees will learn:
What is dbt and why it’s a game-changer for data engineering and analysis
Best practices for data modeling and transformation with dbt
How to integrate dbt into your existing data stack and work with data warehouses such as Snowflake and BigQuery
How to leverage Python packages such as fal to interact with dbt and perform advanced data analysis
Real-world examples and use cases of dbt and Python in action
By the end of this talk, attendees will have a solid understanding of how to use dbt and Python together to build a modern, scalable, and maintainable data stack. Whether you’re a data engineer, analyst, or scientist, this talk will give you the knowledge and tools to take your data infrastructure to the next level.