Revolutionizing Python Notebooks with Marimo
Data Engineering Podcast
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Broad Use Cases and Code Quality
- Marimo is versatile across data engineering, ML research, AI, cybersecurity, and runbook use cases.
- Its reactive DAG encourages better code structure easing research-to-production handoffs.
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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
Parting Question
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
- 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?
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- 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.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story.
- Marimo
- Jupyter
- IPython
- Streamlit
- Vector Embeddings
- Dimensionality Reduction
- Kaggle
- Pytest
- PEP 723 script dependency metadata
- MatLab
- Visicalc
- Mathematica
- RMarkdown
- RShiny
- Elixir Livebook
- Databricks Notebooks
- Papermill
- Pluto - Julia Notebook
- Hex
- Directed Acyclic Graph (DAG)
- Sumble Kaggle founder Anthony Goldblum's startup
- Ray
- Dask
- Jupytext
- nbdev
- DuckDB
- Iceberg
- Superset
- jupyter-marimo-proxy
- JupyterHub
- Binder
- Nix
- AnyWidget
- Jupyter Widgets
- Matplotlib
- Altair
- Plotly
- DataFusion
- Polars
- MotherDuck
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