What it does
JetBrains Datalore is a browser-based notebook environment for data analysis. Its tutorial starts with a workspace and Python notebook, then attaches a CSV, runs code and SQL cells, adds visualizations, and shares or publishes the results. The notebook can include interactive controls for readers, while the plan documentation distinguishes Cloud Free from paid Cloud access. It is a data-science workspace rather than a general desktop IDE.
Build a report from a notebook
Datalore's quick-start tutorial gives a concrete first task: create a workspace, make a Python notebook, and attach a CSV file. Add a code cell to load the file into a DataFrame, then run it from the cell. A SQL cell can query the same file or attached data source, while Markdown cells explain the analysis. For a chart, the Visualize tab can create a starting plot and export it to a code cell when you need to edit or share the chart as part of the notebook.
The tutorial then adds a dropdown control that filters the data and updates the charts. To share the result, choose either notebook collaboration or the report builder; a published report can show selected outputs without exposing the notebook's code to its audience. Link-based edit access is available, so choose the intended permission carefully before sharing.
Datalore is useful for analysts who want code, SQL, charting, and presentation in the same browser workspace. The documentation lists Cloud Free and Cloud plans and describes collaboration and notebook capabilities; the public pricing page did not reveal a current paid amount during this check. It also requires a Datalore account and a supported data connection or file for the tutorial workflow. No workspace, report, or data connection was created for this entry.
Strengths
- The tutorial walks from an attached CSV through Python and SQL cells to charts, interactive controls, and a shareable report.
- Notebook sharing and published reports serve different audiences: collaborators can edit, while report viewers can focus on selected outputs.
- The official plan table includes a Cloud Free option as well as paid Cloud access.
Watch out for
- The workflow is centered on data notebooks and reports rather than general-purpose application editing.
- A user needs a Datalore account and must provide a file or data connection before the notebook can analyze real data.
Best for
- Data analysts preparing reproducible Python or SQL notebooks
- Teams that need to collaborate on analysis and publish interactive reports
- Users who want a hosted notebook editor instead of managing a local Jupyter server
Sources
- Quick start tutorial | Datalore Documentation (Oct 25, 2026)
- Datalore plans | Datalore Documentation (Oct 25, 2026)
- JetBrains Datalore pricing (Oct 25, 2026)
- PyCharm | JetBrains for Data (Oct 25, 2026)