Datalore

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This version includes authentication via HashiCorp Vault for Amazon Athena, multiple notebook schedules support, flexible drag-and-drop, and other improvements.

This version brings native R package support for R users, and elevates your collaborative data science experience with notebook content search, environment variable management, and usability improvements for the Report builder.

The Report builder is a flexible new way to turn your notebooks into beautiful data reports. In this blogpost we’ll give a short introduction to the new features and will share more details in upcoming newsletters!

Data science projects can be complex, consisting of many parts, such as notebooks, data, environments, and scripts, and it can be challenging for data science teams to effectively collaborate on them.

Schedule Jupyter notebooks with just a few clicks. Specify run intervals from the interface or use the cron string to configure a schedule.

Today we are happy to introduce the docker-based installation for Datalore Enterprise and a bunch of other ways to streamline your data science team work. Read on for the highlights of the release!

In the past few months, we’ve been working hard on improving the data exploration and collaboration workflows in Datalore. In this blog post, we’ll give you an overview of what’s new!

Check out the newest data exploration features and enjoy the upgraded interactive reporting experience in Datalore Enterprise 2022.1.

We talk and write a lot about sharing in Datalore as we truly believe in the collaborative nature of data workflows. Today we are happy to introduce a new feature for the result sharing workflow – publishing of Interactive reports.

For the last month, we’ve been working on implementing a frequent user request to export notebooks into different types of files and to share code on other platforms. You might have also already noticed that the look of the editor has changed a bit. Read on to learn more about the recent updates in Datalore 👇🏻