Prague-based software development company JetBrains has launched a new on-premises version of Datalore, called Datalore Enterprise. The new platform makes it easier for data science and data analysis teams to collaborate around shared instances of Jupyter Notebooks. The 30-day free trial version is available here.
Jupyter Notebook has emerged as a really popular tool in recent years. A portmanteau of three programming languages — Julia, Python and R — Jupyter Notebook is a client-based interactive web application that allows users to create and share codes, equations, visualisations, text, etc. JetBrains Datalore is the next best alternative to Jupyter or Google Colab.
“Jupyter notebooks are arguably the most widely used tool in data science. However, when it comes to collaboration, resource management, and security, things aren’t always straightforward,” according to JetBrains blog.
With Datalore Enterprise, users can now take advantage of its powerful code completion, storage integration, authentication, environment and hardware management, version control, and collaboration tools, in their own secure networks.
Smart and secure Jupyter environment for data science teams (Source: JetBrains)
Datalore in detail
JetBrains said, when it comes to configuring Jupyter environments, most teams still create custom solutions around open source Jupyter technology, which takes a lot of time and effort to implement and maintain.
JetBrains Datalore, however, offers seamless code completion, customisation and collaborative experience to its users. Simply put, Datalore brings Pycharm’s IDE, Jupyter Notebook’s design and flexibility, and Google Colab’s collaboration power in one place.
(Source: Seyma Tas)
The core features of Datalore include:
- User authentication: Datalore comes with JetBrains Hub, a tool that allows users to configure almost any possible method of ‘user authentication.’
- Configuring environments: Datalore lets users set up multiple default team environments, and individual users can further dynamically modify the package list for each notebook using Datalore’s library manager.
- Managing hardware and resource usage: Users can connect any hardware server they currently use and make it accessible to users from Datalore’s interface. Additionally, a user can set up internal usage plans to control the resource usage of their team members.
- Connecting data: It comes with ‘persistent internal storage’ for fast access to notebooks and other work artifacts. A user can connect to any database in their Python code. In the near future, it looks to add a native UI for database connections.
- Datalore also supports mounting ‘AWS S3 buckets‘ and lets users store their access credentials in private ‘secret variables.’
JetBrains latest Data Enterprise — a notebook-centric platform — helps data scientists work with code more productively. It features code insight from PyCharm, including code completion, in-app documentation, refactorings, and quick fixes. “This helps you write higher quality code with less cognitive load, allowing you to concentrate exclusively on generating business outcomes,” said the team.
Smart coding assistance in Datalore Enterprise (Source: JetBrains)
Furthermore, to reduce the need to write boilerplate code for visualisations, JetBrains has introduced ‘automatic plot generation‘ for pandas DataFrames. It helps users identify trends in data with less coding. Also, it generates templates of code for line plots, bar plots with an option to customise.
With Datalore Enterprise, JetBrains looks to help data science teams increase productivity and write high-quality code in a safe and secure environment, compared to existing collaboration tools.
The cloud version of Datalore is available here.
In May this year, Microsoft also released Power BI in Jupyter notebooks to embed Power BI reports, dashboards, dashboard titles, report visuals, or Q&A. Also, you can export data from visuals in a Power BI report to the Jupyter notebook for in-depth data exploration and more.