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This is the first of a three-part blog post on the Jupyter Notebook ecosystem. Here, I’ll talk about the data science landscape, and the forces that pushesour tools to evolve.

Ah, Jupyter Notebooks. Love it or hate it, they’re here to stay. The lasttime I wrote about them was two years ago (it’s about running a notebookfrom a remoteserver),and the ecosystem has grown ever since—even how I interact with notebookstotally changed!

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In this multi-part blogpost, I’d review my oft-used tool in the data sciencetoolbox, Jupyter Notebooks, and how I use them in modern times. I dividedthis post into three:

• Part I: The data science landscape(This page). I’d like to look into how the practice of data science has changed for the past few years. Then, I’ll zoom into the three main forces that changed the way I use Notebooks today.
• Part II: How I use Notebooks in 2020. Given these changes, new tools in the Notebook ecosystem emerged. I’d like to share what I like (and don’t like) about them, and how I use them in my day-to-day.
• Part III: Notebooks and the future. Here I’ll share my wishlist for Notebooks, potential gaps that the community can still fill-in, and why Notebooks are still awesome!

To give context, a little more aboutme: as a data scientist, I alternatebetween doing analyses on notebooks and writing production code for dataproducts. My work environment is highly-collaborative so I don’t just reviewcode, I also read (and attempt to reproduce) others’ notebook analyses. Withthat said, I have a strong bias to production code and softwarebest practices, yet I still use notebooks in my day-to-day.

The data science landscape today

The field of data science is rapidly changing. We’venow entered a time where phrases like “sexiest job of the 21stcentury”and “data is the newoil”have become old and replaced by more realistic business problems and groundedtechnical challenges. I see this change as two-fold: we now need to supportboth the (1) demand for productionizing analyses and experiments, and the (2)rapid adoption of the Cloud.

Figure: A simplified framework on how to think about the advancements in
the data science process for the past years

First, the need for production, i.e., creation of data products ordeploying experiment artifacts within the software engineering lifecycle, hasgrown through the years. This is evidenced by an uptake for moreengineering type of work with the rise of machine learning engineers and datascience softwaredevelopers.Furthermore, analyses aren’t confined anymore insidepublications or charts, for there is now a growing demand for experiments to bereproduced and its artifacts to be deployed.

Next, the exponential increase of data necessitates theadoption of the Cloud. We cannot just load a 1TB dataset in pandas usingour own laptops! The popularity of tools like Docker and Kubernetesallowed us to scale our data-processing workloads at unprecedented levels.Cloud adoption means that we take care of scaling, resource provisioning, andinfrastructure when managing our workloads. However, the previous JupyterNotebook ecosystem, as much as it is a staple in the data scientist toolbox,isn’t meant for these changes:

Figure: The Notebooks we know only cover a small domain of the data scienceecosystem

As I’ve said, the Jupyter Notebook we’ve come to know isn’t meant for these changes. They’re meant for exploration, not production. They’re meant to run in a singlemachine, not a fleet of pods. However, for the past five years, the JupyterNotebook ecosystem has grown: we now have JupyterLab, several plugins, new kernelsfor other languages, and third-party tooling available at our disposal. Sure,we can still run notebooks by typing jupyter notebook in our terminals, butit’s now more than that!

This then begs the question: what are the forcesthat prompted these changes?, and how can we leverage this larger notebookecosystem to respond to the changes in data science today?

The three forces of change

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The Jupyter Notebook ecosystem is growing, and I posit that this is driven bythree forces:

• Experiment on the Cloud: big data demands large compute and storage, something that your average consumer-grade machine will not always be capable of.
• Support for developer workflow: more and more data science teams are starting to adopt software engineering best practices—version-control, gitfow, pull requests, and more.
• Quick turnaround from analysis to production: it’s not enough to test hypotheses under controlled environments. Software written for analysis should be easily reused for prod.

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Figure: The growth of the Notebook ecosystem is driven by these forces

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Growing towards a more Cloud-first environment means that we can performNotebook-based tasks in machines more powerful than our own. For example,managed notebook instances enabled us to run Jupyter notebooks from a remoteserver with no-ops and setup. On the other hand, growing towards a moreProduction workflow provides us with a set of tools to endow our notebook-basedtasks with software engineering practices. We’ll see more of these tools in thenext part of this post.

Finally, note that the growth of a tool doesn’t depend on a single entity ororganization. As we will see later on, filling these gaps may stem fromindividuals who contribute third-party plugins or organizations offering managedservices from notebooks.

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Conclusion

In the first part of this series, we looked into the two drivers of growth inthe data science landscape: the (1) adoption of the Cloud, and the (2)increasing demand for production. We saw that Jupyter Notebooks only fill asmall part of this ecosystem, i.e., it’s often used for exploration (notproduction) and only ran in our local machines (not in the Cloud).

Then, using that same framework, we identified three forces of change thatallowed the Jupyter Notebook ecosystem to grow: increased experimentation onthe Cloud, support for developer workflow, and quicker turnaround from analysisto production. These forces may have brought in the development of new tools,plugins, and Notebook-like products to satisfy such gaps.

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For the next part of this series, I’ll talk about how we can use JupyterNotebooks given these forces of change. I’ll review some of the tools andworkflows that have helped me in my day-to-day work and side-projects.