Turning Notebooks Into Collaborative And Dynamic Data Applications With Hex

Summary

Notebooks have been a useful tool for analytics, exploratory programming, and shareable data science for years, and their popularity is continuing to grow. Despite their widespread use, there are still a number of challenges that inhibit collaboration and use by non-technical stakeholders. Barry McCardel and his team at Hex have built a platform to make collaboration on Jupyter notebooks a first class experience, as well as allowing notebooks to be parameterized and exposing the logic through interactive web applications. In this episode Barry shares his perspective on the state of the notebook ecosystem, why it is such as powerful tool for computing and analytics, and how he has built a successful business around improving the end to end experience of working with notebooks. This was a great conversation about an important piece of the toolkit for every analyst and data scientist.

Announcements

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Your host as usual is Tobias Macey and today I’m interviewing Barry McCardel about Hex, a managed platform to turn your notebooks into collaborative, interactive data apps and stories

Interview

Introductions

How did you get introduced to Python?

Can you start by describing what you have built at Hex and your motivation for starting the business?

Who are the primary users of the Hex platform?

How has that focus influenced your product direction and the features that you prioritize?

What are the biggest roadblocks that you see data analysts and data consumers running into?

How have those roadblocks shifted in recent years?

What is it about the concept of a notebook that has caused them to see such a massive rise in usage and popularity?

What are the barriers to productivity and accessibility that still exist in the notebook ecosystem?

What are the pieces for working in and with notebooks that are still missing?

What does Hex add to the experience of working with notebooks?

Can you describe how the Hex platform implemented?

How has the design of the platform changed or evolved since you first began working on it?

Where does Hex sit in the lifecycle of notebook creation and usage?

How does it compare to other services built to support users of notebooks such as Zepl, Saturn Cloud, Noteable, etc.?

You focus on the Jupyter platform, but there are a number of other notebook frameworks that have sprung up in recent years. What do you see as being the relative strengths of the available options?

What are the trends in the tooling, capabilities, and use cases for notebooks that you are keeping an eye on?

What are the most interesting, innovative, or unexpected ways that you have seen the Hex platform used?

What are the most interesting, unexpected, or challenging lessons that you have learned while building Hex?

When is Hex the wrong choice?

What do you have planned for the future of the Hex business and product?

Keep In Touch

LinkedIn

@TheRealBarryM on Twitter

Picks

Tobias

Flakehell

DC Extended Universe Movies

Barry

Wingspan

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.

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Links

Hex

Palantir

IPython

Podcast Episode

Jupyter

Mathematica

IDE == Integrated Development Environment

nbconvert

Observable Javascript Notebooks

React

BlueprintJS

Papermill

Streamlit

Podcast Episode

Shiny

Redshift

Snowflake

Data Engineering Podcast Episode

BigQuery

PostgreSQL

Data Engineering Podcast Episode

Noteable

Saturn Cloud

Zepl

Zeplin Notebooks

JupyterHub

Binder

Kubeflow

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA