12-factor application principles and How to build by-them

12-factor application principles and How to build by-them

Table of Contents
  1. Codebase
  2. Dependencies
  3. Config
  4. Backing services
  5. Build, release, run
  6. Processes
  7. Port binding
  8. Concurrency
  9. Disposability
  10. Dev/prod parity
  11. Logs
  12. Admin processes

These 12 principles are the core of application which are designed to as a service, or as it’s known for in short SaaS.

Kubeexperience Chapter 2 - this was my take on the 12-factor application principles back in 2020

today I’m gotin to the 15-factor application principles, which are based on the 12-factor application principles in addition to some cloud native principles, not just SaaS …

2023 take on the 12-factor application principles

To be continued …

comments powered by Disqus

Related Posts

IaC & GitOps with EKS blueprints

IaC & GitOps with EKS blueprints

Originally posted on the Israeli Tech Radar on medium.

TLDR; Need a cluster up and running fast? Take a close look at eks-blueprints, I got started in minutes and have been working with it for almost 2 years now.

Read More
Why metrics are-important ?! | Meetup slides

Why metrics are-important ?! | Meetup slides

A brief of a tech talk about the Backstage project and how it can be used to build an engineering platform.

Read More
Developing a Webcam Arcade Controller using Deep Learning by TensorFlow & Keras - part 1, Meetup

Developing a Webcam Arcade Controller using Deep Learning by TensorFlow & Keras - part 1, Meetup

We will introduce Deep Learning, demo a DL model in action, introduce an architecture for training and use of such model in a production environment, and show some critical sections of the code. Demo - Control video game using Deep Learning (15 min) - by Haim Cohen, Big Data Architect from Tikal. We will demo an application which makes use of deep learning in order to control a video game through webcam and head gestures. Lectures: Deep Learning - Starting Now (20 min) - by Shai Tal, Data Scientist and Machine Learning Engineer from Tikal. Deep learning is a tool. And tools need to be understood. We will briefly discuss the practical benefits of machine learning over programming, and the benefits of deep learning over classic machine learning for building visualisation and NLP models. Deep Learning API’s & Architecture (30 min) by Haim Cohen. We will Introduce TensorFlow & Keras through code examples, go through main parts of the demo application and talk about the architecture of the demo application and other Deep Learning based systems. DevOps Concerns for Deep Learning Systems (30 min) - by Haggai Philip Zagury, DevOps Architect from Tikal.

Read More