Unleashing the Power of dbt and Python for Modern Data Stack
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 demonstrate the full potential dbt Python models. 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 are dbt Python models and why they are a game-changer for data engineering and analysis
– Best practices for data modeling and transformation with Python and dbt
– How to integrate dbt Python models into your existing data stack
– 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.
Meder Kamalov holds a PhD in Organic Chemistry and has several years of academic experience in chemical research. However, his passion for programming led him to transition to a career in software development. With over four years of experience as a software developer, he has honed his skills in building data pipelines, implementing machine learning models, and developing web applications.