Private BI for a large internet marketing firm, incorporating data from Google, Bing, and a plethora of affiliate partners

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  • We used RabbitMQ in conjunction with Celery (Celery-Beat package) to run periodic tasks on the server.


  • We used celery, in combination with RabbitMQ and celery-beat, to run periodic tasks, as well as some user-initiated long-running tasks on the server.


  • The client used bugherd to post bugs found in production.


  • We hooked New Relic up to monitor the health or our servers and containers, as well as track the actual app performance.


  • Our backend was written in Django. We took advantage of the ready-to-go admin interface as a go-to solution for the client to be able to authorize his users, as well as other functionality, while most of the work was done through the Django Rest Framework.


  • Django REST delivered all the content to the BI, making calls to the Postgres DB, aggregating numeric data, and automatically associating data models at the time of row creation.


  • The front end was built on an Angular template supplied by the client. We leveraged Angular's flexibility and speed to delivered complex matrices of data quickly and with great finesse.


  • Each component of the app was launched in a separate container, so that they wouldn't have to share resources: the front end in one, the back end in another, a third for celery, a fourth for celery-beat, and a fifth for RabbitMQ. Actually, we ended up running four front-end containers and eight back-end, due to load constraints.


  • We used Express to serve up our Angular front-end.


  • PyCharm is our preferred IDE for python apps, for all its simple awesomeness in writing code, as well as the ease with which you can run a Django shell, a web server, or run tests.


  • We used Redis to cache server requests, which cut down response times on most requests by at least 1/10.


  • Our server nodes we hosted on EC2


  • Kubernetes was our go-to container managing tool.


  • We moved our database from compose.io to AWS for speed and price.



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