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ยท 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, authorization...
  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-nativeโ€‹

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.

ยท 4 min read
Eric Charles

Crossplane is an open source Kubernetes add-on that enables platform teams to assemble infrastructure from multiple vendors, and expose higher level self-service APIs for application teams to consume, without having to write any code. It allows you to compose cloud infrastructure and services based on XRD (cross resource definitions) that extends the existing Kubernete CRD (Custom Resource Definition). To achieve this awesome goal, you have to use various repositories that reside in the GitHub crossplane, crossplane-contrib and upbound organisations. As adaptor of that new technology, you can rely the official documentation where a lot of details are gathered.

To ease our understanding and document our experiments, we have created a crossplane-example repository that will take you step-by-step to use Crossplane to deploy your infrastructure on top of Google Cloud and also develop a user interface and Helm chart that access a database created by Crossplane.


Crossplane community is welcoming, just like the Crossplane logo is fun!