What Can You Do with JetBrains Datalore Notebooks?

Updated

JetBrains Datalore is a collaborative data science platform built around Jupyter notebooks. With it, you can write and run Python, SQL, R, and Scala code in notebooks, get smart coding assistance, execute code on cloud CPUs and GPUs, and collaborate with your team on shared results. It fits teams that want notebook work to be shared and reproducible rather than trapped on one laptop, and it also offers an On-Premises option for organizations that need to connect their own identity provider.

Write and run code in Jupyter notebooks

Datalore is a notebook environment, so the core workflow is familiar: you write code in cells, run them, and see outputs inline. What it adds is multi-language support in one place — Python, SQL, R, and Scala — so a single notebook can mix, for example, SQL queries against a database with Python analysis of the results.

Smart coding assistance is part of the editing experience, aimed at helping you write code faster and with fewer errors as you work through a notebook.

Run on cloud CPUs and GPUs

Instead of being limited by your local machine's hardware, Datalore lets you run code on powerful CPUs and GPUs in the cloud. This matters when a notebook involves heavy computation or model training that would be slow or impossible locally. You choose the compute resources for the job rather than upgrading your own hardware.

Collaborate and share results

Datalore is described as a collaborative data science platform, which is the main difference from a plain local Jupyter install. Team members can work together in notebooks and share results, so analysis becomes something a group can review and build on instead of a file passed around by email.

Access and deployment options

You reach Datalore by logging in — either with a Google account or a JetBrains Account — and you can create an account if you don't have one. For organizations with stricter requirements, Datalore On-Premises supports connecting identity providers such as Okta, SAML, and AzureAD.

Pricing is handled separately on JetBrains' pricing page, so check there for current plans and what each includes before committing.

When Datalore is a good fit

  • You work in Jupyter notebooks and want Python, SQL, R, and Scala in one environment.
  • Your analysis needs more CPU or GPU power than your local machine provides.
  • You need teammates to collaborate on notebooks and share results.
  • Your organization requires an on-premises deployment with its own identity provider.

If your work is a single notebook on your own machine with no sharing or heavy compute needs, a local Jupyter setup may be simpler. If collaboration, cloud compute, or multi-language notebooks are the point, Datalore is built for exactly that.