Setting up an analytics platform with python

By combining the power of docker and python I can create an analytics platform that will always run and is not dependent on the versions of python or anything other I run on my laptop.

I will show how easy it is to setup jupyter notebooks and use them for analytics. And how easy it is to publish an analysis to this blog. Docker gives me at least two benefits, 1. I can be sure that when I start my docker image the analysis will always run because docker makes sure the versions of all the software are the same. 2. If my laptop is not enough to run an analysis I can deploy the same docker image to a much more power full computer in a cloud.

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Docker setup – part 3: setting up a simple Continuous Deployment pipeline using bitbucket and docker

This part ended up being a lot more involved and much longer than I expected. In this part my goal was to make the production deployment process smoother, make it easier to do development on the setup and prepare it to be more redundant.

Before I started, the production deploy process was to connect my IDE(phpstorm), with it’s docker-compose integration, to my remote docker and run a “docker-compose up”. This workflow has some drawbacks

  • It is easy to deploy to production by error since it is only a single click in the wrong place – so I can’t be sure that the running version maps to a specific revision in my git
  • When I deploy, the containers are taken down until the new containers are booted, causing service disruption
  • There are no easy way to do a revert, if I deploy a setup with errors, it is a manual process to revert it to a previous version
  • I would like to work with Continuous Integration / Continuous Deployment (CI/CD) as a pipeline for a more smooth workflow which requires a more tight control of the process
  • I don’t have an easy way control differences between development and production
  • Also I would like to prepare to work with replicated services where the service would have a fall over if an error crashes a container.

Much of this part are to prepare the setup to solve the challenges above. I did not manage to solve all of the problems up front, which disappointed me a bit, but it will be solved in later parts.

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Docker setup – part 1: Setting up state of the art infrastructure for personal projects

My day-job is developing and managing websites and e-commerce platforms, so I know the importance of stability and scalability on web platforms and technology in general. This is also true when working with my personal projects. For some time I have wanted to push my projects to the cloud so I can more easily utilize the scalability there. Also I only have a laptop, and I don’t want a desktop computer to take space, collect dust and consume power. Hardware also gets old and needs to be maintained. All those worries I would like to skip.

My goal is to setup a platform that will do a few things for me:

  • Allow me to run my blog – this site
  • Allow me to run my girlfriends blog – this site
  • Be easy to update wordpress since it is known to have security issues in older versions
  • Have good backup
  • Alert me if/when the sites are down
  • Alert me if/when the sites experience high load
  • Protect the sites against ddos and similar attacks
  • Provide me with a coherent platform for all my projects both web and data analysis
  • Be a learning platform for new technology in cloud computing

Lofty goals but I hope to setup a platform that will service me for many years.

 

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