Datalore vs JupyterHub

Datalore is a collaborative data science platform for exploring, analyzing, visualizing and sharing data. Its Jupyter-compatible notebooks provide coding assistance for Python, Kotlin, Scala and R, with native SQL cells that can transfer query results into pandas DataFrames. Teams can edit code together in real time and share notebooks with view or edit access. Notebooks can become data stories or static and interactive reports. Datalore AI can help write Python, SQL and R code, describe findings and fix errors. Database connections include Amazon Redshift, Azure SQL Database, MariaDB, MySQL, Oracle, PostgreSQL and Snowflake; AWS S3 and Google Cloud Storage buckets can be mounted as notebook folders. The free plan includes 120 CPU S machine hours and 10 GB of cloud storage. The Cloud plan costs $35 a month, billed monthly. On-Premises deployment runs on 64-bit Linux using Docker Compose or Kubernetes, but not on ARM, macOS or Windows hosts. JetBrains states that Datalore deployments are SOC 2 Type II certified and GDPR compliant.

JupyterHub gives groups access to computational environments without requiring each person to install and maintain one. It creates a separate Jupyter environment for each user, and starts, manages and proxies that user's notebook server. It can serve interfaces such as Jupyter Notebook, JupyterLab, RStudio and nteract, with authentication configured through pluggable options including OAuth and GitHub. Organizations can run it in the cloud or on their own hardware, including virtual machines or a laptop. The Littlest JupyterHub targets a small, single-server setup, while Zero to JupyterHub with Kubernetes is designed for larger cloud deployments and includes a Helm chart and deployment guide. The project describes JupyterHub as container-friendly and Kubernetes-compatible, with capacity for up to tens of thousands of users. Its REST API supports administration of the Hub, users and services. JupyterHub is open-source software released under a modified BSD license and is free to use. It supports Python, R, Julia and Scala, with ipynb notebooks, and is intended for classrooms, research groups and corporate data science teams.