HackerNews Readings
40,000 HackerNews book recommendations identified using NLP and deep learning

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Web Scalability for Startup Engineers

Artur Ejsmont

4.8 on Amazon

6 HN comments

Building Secure and Reliable Systems: Best Practices for Designing, Implementing, and Maintaining Systems

Heather Adkins, Betsy Beyer , et al.

4.7 on Amazon

6 HN comments

Show Me the Numbers: Designing Tables and Graphs to Enlighten

Stephen Few

4.5 on Amazon

6 HN comments

Database Internals: A Deep Dive into How Distributed Data Systems Work

Alex Petrov

4.7 on Amazon

6 HN comments

Whatever It Takes: Master the Habits to Transform Your Business, Relationships, and Life

Brandon Bornancin

4.8 on Amazon

6 HN comments

Kubernetes: Up and Running: Dive into the Future of Infrastructure

Brendan Burns , Joe Beda, et al.

4.6 on Amazon

6 HN comments

Python for Kids: A Playful Introduction to Programming

Jason R. Briggs

4.6 on Amazon

6 HN comments

The Phoenix Project (A Novel About IT, DevOps, and Helping Your Business Win)

Gene Kim

4.7 on Amazon

6 HN comments

Terraform: Up & Running: Writing Infrastructure as Code

Yevgeniy Brikman

4.6 on Amazon

6 HN comments

A World Without Email: Reimagining Work in an Age of Communication Overload

Cal Newport, Kevin R. Free, et al.

4.5 on Amazon

5 HN comments

Discovering Statistics Using R

Andy Field, Jeremy Miles , et al.

4.5 on Amazon

5 HN comments

C++ Crash Course: A Fast-Paced Introduction

Josh Lospinoso

4.7 on Amazon

5 HN comments

Irresistible: The Rise of Addictive Technology and the Business of Keeping Us Hooked

Adam Alter and Penguin Audio

4.6 on Amazon

5 HN comments

Kafka: The Definitive Guide: Real-Time Data and Stream Processing at Scale

Neha Narkhede , Gwen Shapira, et al.

4.4 on Amazon

5 HN comments

The Great Reset: How Big Tech Elites and the World's People Can Be Enslaved by China CCP or A.I.

Cyrus Parsa and The AI Organization

4.5 on Amazon

5 HN comments

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bcheungonMar 26, 2020

I found the Udemy CKA / CKAD certification courses to be good, even if you are not interested in certification. Most of the books I have come across have been good as well but Kubernetes moves pretty fast so some of the YAML files might need to have slight tweaks.

antisocialonApr 2, 2020

Yes, I highly recommend Nigel Poulton's Kubernetes course on Pluralsight, as well as Janani Ravi's ML courses.

dankohn1onMar 23, 2019

This is particularly well-liked for a more in-depth review of Kubernetes: https://github.com/kelseyhightower/kubernetes-the-hard-way

andrewstuart2onApr 27, 2021

To be fair, the authors of Kubernetes were pretty clear from very early on that it wasn't expected to scale to "one cluster to rule them all," at least in part since that's not how the system it was designed to replicate/replace worked. I don't think that's changed, but at the same time I think there was always an idea that the same concepts used to orchestrate e.g. pods and services, etc, could be extended to the clusters themselves. From what I can tell at a quick glance, that's exactly what karmada looks like it's intending to do.

raesene9onAug 20, 2017

If you want to get started with Kubernetes I'd recommend either, Kelsey Hightower's Kubernetes the hard way (https://github.com/kelseyhightower/kubernetes-the-hard-way) which is a good way to start getting an understanding of the various components and how they fit together or, if you want to try out running workloads on a cluster, Kubeadm (https://kubernetes.io/docs/setup/independent/create-cluster-...)

If you're interested in books on the topic, I like Kubernetes in Action (https://www.manning.com/books/kubernetes-in-action) or Kubernetes: Up and running (http://shop.oreilly.com/product/0636920043874.do)

One of the challenges with Kubernetes is that it's pretty fast moving, so it's a good idea to work with resources that are up to date. I know a last commit from May doesn't seem very old but that's going to a least be missing 1.7 and could be missing 1.6 or earlier as well.

ben_jonesonFeb 27, 2017

If anything it proves how much mismanagement / wasted potential software organizations have had in the last 20 years. Full automation should be the natural progression of our trade but I fear most companies stall after 5ish years due to turnover, brain-drain, re-organizations, acquisitions, management incompetency etc. Google on the other hand has always had a seemingly never ending pool of resources and talent to keep pushing the barrier further. Fortunately they give a lot back to the community in the form of books, talks, and projects such as Kubernetes (a poor man's Borg). However I fear that with all things commercial it will lead to an oligarchy where companies like Google, Facebook, and Uber, are just that far ahead of the curve nobody else will ever catch up.
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