In this conversation, Akshay Agrawal, Co-founder and CEO of Marimo, introduces a groundbreaking open-source Python notebook environment. He tackles the drawbacks of traditional Jupyter notebooks, such as hidden states, and showcases Marimo’s reactive execution model and improved interactivity. Akshay also discusses the tool's capability to seamlessly integrate data apps, compared to other platforms like Jupyter and Streamlit. The talk highlights the technical architecture, community-driven development, and exciting future plans, including AI enhancements, aiming to revolutionize data workflows.
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question_answer ANECDOTE
Akshay's Data Journey
Akshay worked on TensorFlow 2 at Google Brain before pursuing a PhD focused on machine learning research.
His experience with data and ML informed his motivation to build better interactive computational tools.
insights INSIGHT
Marimo's Reactive Python Approach
Marimo is a reactive Python notebook that ensures code and outputs are always consistent to avoid hidden state problems.
It stores notebooks as pure Python files with built-in UI widgets, enhancing interactivity and reusability.
insights INSIGHT
Power of Python-First Design
Leaning into Python allows Marimo to exploit Python AST for static analysis and reactive execution.
Python-first design enables script execution, module reuse, version control, and enhanced developer UX.
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Summary In this episode of the Data Engineering Podcast Akshay Agrawal from Marimo discusses the innovative new Python notebook environment, which offers a reactive execution model, full Python integration, and built-in UI elements to enhance the interactive computing experience. He discusses the challenges of traditional Jupyter notebooks, such as hidden states and lack of interactivity, and how Marimo addresses these issues with features like reactive execution and Python-native file formats. Akshay also explores the broader landscape of programmatic notebooks, comparing Marimo to other tools like Jupyter, Streamlit, and Hex, highlighting its unique approach to creating data apps directly from notebooks and eliminating the need for separate app development. The conversation delves into the technical architecture of Marimo, its community-driven development, and future plans, including a commercial offering and enhanced AI integration, emphasizing Marimo's role in bridging the gap between data exploration and production-ready applications.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I'm interviewing Akshay Agrawal about Marimo, a reusable and reproducible Python notebook environment
Interview
Introduction
How did you get involved in the area of data management?
Can you describe what Marimo is and the story behind it?
What are the core problems and use cases that you are focused on addressing with Marimo?
What are you explicitly not trying to solve for with Marimo?
Programmatic notebooks have been around for decades now. Jupyter was largely responsible for making them popular outside of academia. How have the applications of notebooks changed in recent years?
What are the limitations that have been most challenging to address in production contexts?
Jupyter has long had support for multi-language notebooks/notebook kernels. What is your opinion on the utility of that feature as a core concern of the notebook system?
Beyond notebooks, Streamlit and Hex have become quite popular for publishing the results of notebook-style analysis. How would you characterize the feature set of Marimo for those use cases?
For a typical data team that is working across data pipelines, business analytics, ML/AI engineering, etc. How do you see Marimo applied within and across those contexts?
One of the common difficulties with notebooks is that they are largely a single-player experience. They may connect into a shared compute cluster for scaling up execution (e.g. Ray, Dask, etc.). How does Marimo address the situation where a data platform team wants to offer notebooks as a service to reduce the friction to getting started with analyzing data in a warehouse/lakehouse context?
How are you seeing teams integrate Marimo with orchestrators (e.g. Dagster, Airflow, Prefect)?
What are some of the most interesting or complex engineering challenges that you have had to address while building and evolving Marimo?\
What are the most interesting, innovative, or unexpected ways that you have seen Marimo used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Marimo?
When is Marimo the wrong choice?
What do you have planned for the future of Marimo?
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
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