What’s new 2022.3 (November)

Report builder revolutionizes the way you build interactive reports from notebooks in Datalore. Arrange the cells on the canvas, easily adjust their height and width, hide unnecessary steps, and share a static or interactive report with your stakeholders via a link.

Connect your Jupyter notebook to Google BigQuery directly from the Datalore interface and enjoy SQL code completion, automatic plots and statistics for query results, and a seamless transition to Python.

Apply filtering and sorting to Pandas DataFrames and SQL query results directly in the cell output. Select the columns to display, sort the dataset by a specific column, filter based on “equals” and “contains” expressions, and easily jump to the top or bottom of the dataset. After you complete the filtering and sorting, use the Export to code cell option to generate the Pandas code snippet and make the table view reproducible.

Make certain numbers stand out in your notebooks and reports with a Metric cell. Enable automatic comparison to a certain variable or to the previous cell run results.

With Date cell, your notebook collaborators and report users can choose a specific date or a date range from the user interface. In addition, you can use the selection inside Python code and parametrize your analysis results as follows:

Connect to your remote databases using SSH tunneling in Datalore. This will create an encrypted SSH connection between Datalore and your gateway server. Connecting via SSH tunnels makes it possible to connect to databases that are not exposed to the public network.

Easily manage who can access notebooks and workspaces via group sharing. Create user groups inside Datalore’s Admin panel or sync them with the JetBrains Hub authentication module. JetBrains Hub can sync user groups for selected authentication modules with your authentication provider (e.g. Azure AD). Find more information on supported Auth modules here.

Add AWS S3 and GCS buckets from the same interface as the SQL databases. Browse the bucket’s file system, read and write to the bucket with code, and work with the attached bucket as with a mounted folder.