Data collaboration isn’t just for technical teams anymore.
From pair programming and code review to sharing analysis with anyone with a “C” in their title, today’s data professionals constantly collaborate both inside and outside their teams. The hard part is doing it quickly and at scale.
Let’s look at how teams use modern data notebooks to streamline data collaboration and work together more efficiently.
Data collaboration isn’t black or white — it’s more like a gradient with lots of shades of gray.
Sometimes you want to give a teammate full access to your work (edit my code!). Other times you want them to look but not touch (hands off my code!). And then there are times when it’s somewhere in between.
In any case, you’ll face two challenges: sharing your work in a secure way and avoiding the dreaded copy-paste rigamarole. Cloud-based data notebooks nip both problems in the bud.
The granular permissions found in modern notebooks allow you to invite colleagues to shared workspaces and give them specific levels of access, such as:
- Guests who can view notebooks
- Viewers who can leave comments on projects but not make any changes
- Contributors who can execute a notebook and toggle widgets but not rewrite code
- Editors who can fully edit notebooks as well as manage connections to data sources
- Administrators who can do all of the above in addition to managing who’s allowed to access different workspaces and which data sources are enabled and shared
API keys, database connection details, and other sensitive information are encrypted and stored securely. When more technical collaboration is on the menu, teammates with the right permissions can jump straight into your project without having to hunt down credentials or get permission to access a database.
Translation: No more caving in to the temptation to share passwords out in the open when you want to collaborate on a project (your friendly neighborhood IT security director will thank you).
But permissions aren’t just about security — they’re about convenience.
Data work often ends up as a deliverable: a chart, a report, a question answered. It’s communicated with someone and a lot of the time that someone has no interest in seeing your messy code.
Cut to you dragging charts into a document, saving it as a PDF, emailing it out, and bracing yourself for the inevitable follow-up that means you’ll have to start the whole process over again.
Since modern data notebooks aren’t locally hosted, they eliminate the need to switch back and forth between different formats to satisfy technical and non-technical collaboration. You can grant a stakeholder permission to view or comment on a notebook and use the same interface you used for your nitty-gritty data analysis as the final deliverable by simply hiding the code blocks (or publishing it as an article or interactive app).
That also means not starting from scratch each time you need to refresh the data — just re-run it in the background and watch your non-coding colleague clap with joy (or, better yet, schedule it to re-run itself).
The ability to use notebooks as an all-in-one tool for analyzing data, visualizing results, and providing context with copy is why companies such as Slido count on them for data collaboration.
“Since metrics require a lot of input from subject matter experts, data consumers, and business stakeholders to define and align on definitions, we needed a collaborative layer where we could get immediate feedback,” said Slido’s Head of Analytics Engineering Michal Koláček.
Conquer multiplayer collaboration
You could write an entire article on how data teams collaborate (ahem, we did). The main takeaway? It comes in many flavors.
But most tools limit how and when team members can work together. Data teams must often play a game of “Is it my turn yet?” (followed by an equally rousing game of “Please tell me you didn’t overwrite my work”).
But there are no limits with cloud-based data notebooks. Multiple team members can share the same environment simultaneously (it’s always nice to divide and conquer). That makes it much easier for companies such as VantAI to iterate quickly.
“Working in Deepnote is like code review and rapid prototyping at the same time, saving valuable time in the iteration cycles,” said VantAI CTO Luca Naef. “But as opposed to code review via GitHub, you have direct access to the runtime and program state, which makes understanding complex models much easier and leads to much more spontaneous creative ideas."
And when real-time collaboration isn't a must-have, teams can use comments to work together asynchronously.
This is especially helpful when collaborating with business stakeholders. After all, data wranglers aren’t always the domain experts. Team members and stakeholders alike can add, edit, delete, and re-open comments during the exploratory analysis phase instead of waiting to share their questions and concerns at the end.
Protect & reproduce projects
Versioning is top of mind for data professionals. Why? It’s important for safeguarding work, it’s essential for reproducibility — and it’s often a big pain in the neck.
But not if you’re using a modern data notebook.
Saving a new version of a notebook is as simple as clicking a button and moving on with your life (month-long training on how to use GitHub not required). Or just let the notebook save for you automatically at preset intervals.
Need to restore an older version? You automatically create a snapshot of the current state when you go back to a previous notebook (no lost code for the win). Meanwhile, any changes made by collaborators are automatically tracked and can be instantly pulled up in your action log.
The ability to quickly access past projects, duplicate them, and use versioning to revert to past states is one major reason a Deepnote customer — a hedge fund with a large data team — relies on notebooks.
“Deepnote allowed us to gradually experiment with collaboration at little cost and slowly scale our collaborative workflows,” the customer said. “Ultimately, it has made a huge difference for collaboration in our team — it's night and day. There's before Deepnote and after Deepnote time."
If analysis is done in a forest, and no one is around to see it, does it exist? Sorry to get metaphysical.
No matter what insights you uncover, tools that create silos and fragmented workflows make it hard to take action on them. That’s where modern data notebooks come in.
Instead of downloading a notebook, exporting it, and dealing with the limitations of a static document, projects can be shared with a link (or viewed within your workspace by inviting someone over email). Easy peasy.
But what about when you need to turn your chaotic notebook into a beautiful, easy-to-understand presentation? We’ve all been there. You share your analysis with a non-technical colleague, they see a line of code, and their eyes begin to glaze over.
No more racing to save charts as PNGs and drag them into a Google Doc. With modern data notebooks, you can share a link to the notebook or just hit publish (and to all you
nbconvert-to-html folks, we see you, but one-click publishing from the cloud is faster and more secure). You can easily turn notebooks into interactive articles, dashboards, and apps, complete with customizable layouts and access rights to control who can view them.
This is why companies such as Webflow use notebooks to help teams across their organization collaborate on data projects.
“We use this a lot for experimentation,” said Webflow’s Senior Manager of Data Science & Analytics Allie Russell. “Data flows in and gets modeled for experimentation, then we make it interactive and available for product managers and data scientists to use.”
Data teams don’t do their best work alone. Exploring data and extracting value from it is a team sport — and those teams are only getting bigger.
That’s why leading data teams use notebooks to take their teamwork to the next level.
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