Skip to main content

3 posts tagged with "jupyter"

View All Tags

· 6 min read
Eric Charles

We are thrilled to announce the 0.0.6 release of Datalayer. This release improves the data analytics user and developer experience with Jupyter React, a javascript library to ensure React.js is a first-class citizen in the Jupyter ecosystem.

Datalayer React is built on top of JupyterLab which aims to be the next default notebook for Python data scientists and is actively developed. However, some users sill prefer the classic notebook and JupyterLab is not yet mainstream... The following points can be the identified as the source of the shadow:

  1. The user interface is intimidating and quite complicated. An initiative to strip-down the user interface has been taken with Retrolab, but the result still looks pretty much like JupyterLab without visible value compared to the classic notebook. Users will even loose some beloved features like their preferred keyboard shortcuts, VIM mode, performance...
  2. The extensions ecosystem is rich but breaking changes in the core of JupyterLab have made the overall ecosystem fragile and subject to failures on installation.
  3. The overall performance (startup time, load large notebook, switch tabs...) is know to be degraded on JupyterLab.
  4. The recently merged realtime collaboration feature is solely not usable with a real multi-user authentication and authorization system.
  5. As developer, the Lumino widget toolkit which backs JupyterLab user interface is hard to use and looks pretty much like a Qt toolkit rather than like a modern javascript e.g. React.js, Vue.js, Svelte...
Jupyter React Widgets Gallery

· 4 min read
Eric Charles

All Data Scientists know that story... Install the well-known Jupyter Classic or JupyterLab Notebook on their local PC/laptop, pip install some python libraries like pandas..., download some datasets and finally start analysing with a notebook in isolation. There are a few pain points there:

  1. Setting up the tools is hard and time consuming. You have to install Python, Jupyter and add the libraries you need. Conda environments or Docker containers can help mitigate the pain at some point, but finally these are yet additional tools to setup and manage.
  2. At some point, they want to collaborate with teammates, or want to share some results. The Data Scientist is just on his island and has no easy way to break the silo. The recent Realtime collaboration features have been merged into JupyterLab but it is just the permises and miss fundamental building blocks like identity, authorisation...
  3. The analysis is not easily reproducible. The setup you have done on a particular Windows platform is completely different from the setup another collaborator may have done on MacOS.

More Cloud-native

There comes the need for an better solution. At Datalayer we think that a more Cloud-native Jupyter can help remove those pain points. In other words, we embrasse the infrastructure provided by cloud providers like GCloud, AWS, Azure... and build on top to provide more power to the Data Scientist.

Cloud native computing is an approach in software development that utilizes cloud computing to "build and run scalable applications in modern, dynamic environments such as public, private, and hybrid clouds.
Wikipedia https://en.wikipedia.org/wiki/Cloud_native_computing

· 2 min read
Eric Charles

Since our last blog post on January 2018, we have changed a lot the Datalayer architecture. Back in 2018, we had chosen for Apache Zeppelin for its good integration with Big Data frameworks like Apache Spark and competely replaced the existing Angular.js user interface with a home-brewed React.js implementation to integrate with the Kubernetes Control Plane. While rolling out more and more features on top of our former version 0.0.1, we have been intrigued in February 2018 by JupyterLab being announced to be ready for users. Back in time, in July 2016, JupyterLab was positioned as the next generation of the Jupyter Notebook.