dbt brings best engineering practices into the world of data & analytics to help you build out a solid foundation. Data science notebooks help you leverage well-modelled data and create chaos in exploratory workflows. To make any data endeavors successful, you need both - tools that gives you strong foundation coupled with tools that help you experiment and break things. In this talk, we will introduce Deepnote, a collaborative data science notebook and how you can use it with dbt to introduce more collaboration into your team’s data modelling and exploratory workflows.

**Talk outline**

1. The case for notebooks as a part of the modern data stack
1a. Forces that are in play that make the notebooks paradigm relevant today
1b. The need for collaborative data workflows and how notebooks can help
2. Introducing Deepnote
2a. Our mission - bringing data teams together to explore, analyze and present data from start to finish
2b. Quick demo (SQL cells as a first-class citizen, real-time collaboration, integrations, code intelligence that helps you write production-ready code)
3. Using dbt with Deepnote
3a. How teams can operationalize Deepnote with dbt to introduce more collaboration into their engineering + exploratory workflows
3b. The exact project is still work in progress, we're defining this with our community. The foundational project can be found here: https://deepnote.com/project/Using-dbt-in-Deepnote-EVTMFcUdQ1WKrzGECkKzLA/%2Fnotebook.ipynb/#00012-88c753c7-8114-45ca-b36c-727a2bb51989