Welcome to the second post in a series of “How to Scale your AWS Infrastructure”. In the first post, we talked about horizontal scaling, autoscaling, CI/CD, infrastructure automation, containerization, etc. In this post, we will continue the discussion around databases, loose coupling, caching, CDN, etc. Let’s start the discussion with database scaling.

The cloud offers unparalleled flexibility. However, that flexibility comes at a cost. The amount of moving pieces increases. The environment becomes more heterogeneous. So, if you want to stay on top of things, you need a more comprehensive view of your cloud infrastructure. After all, you don’t want your customers to realize that something has gone awry before your people do. In this post, I’m going to talk about cloud monitoring.

Dad jokes lead the way in this episode as we interview Chris Martello, manager of application performance at Cengage. Chris is a wearer of many testing hats, but his passion is chaos and breaking things on purpose. Chaos was a natural fit for Chris with his background as a middle school science teacher, so when he made the jump to tech chaos engineering was a natural fit.

As a software engineer and technical content creator, I work with a lot of companies on many different contracts. To get paid for my work, most companies require that I send an invoice. Sometimes they want one daily, at the end of the week, or even when the project has been completed. Sending an invoice to my clients is crucial because it determines when and if I will get paid on time. If this sounds like a repetitive task that can eat deep into my productive hours, you are right.

Database backup protects your data by creating a copy of your database locally, or remotely on a backup server. This operation is often performed manually by database administrators. Like every other human-dependent activity, it is susceptible to errors and requires lots of time. Regularly scheduled backups go a long way to safeguarding your customers’ details in the case of operating system failure or security breach.

Observability is a new term that’s slowly entered the mainstream over the last two years. Today it’s used in the context of monitoring, but it’s much more than that. And it also goes way beyond visibility. So, in this blog, we set out to explore observability vs visibility and find out, what’s the difference? In a recent podcast, our friends at Riverbed neatly explained that seeing and observing are two different things, and can be compared to hearing vs listening.

If you look back to the state of technology only a few decades ago, you’ll stumble upon a technological world full of silos and frustration. Two of an organization’s most vital teams—software development and IT operations—struggled to communicate effectively. Departments resided within silos, and development and testing were conducted at a snail’s pace.

The CNCF Annual Survey 2021 is in and makes for some very encouraging reading for the future of Kubernetes and its place in the tech landscape. The 2021 survey was the biggest yet, with some 3,829 developers, engineers, architects, and C-level execs in the cloud native space taking part. Here are some of our key takeaways…

Jupyter Notebook is a web-based interactive computational environment for creating notebook documents. It supports programming languages – such as Python, Scala, R – and is largely used for data engineering, data analysis, machine learning, and further interactive, exploratory computing. Think of notebooks like a developer console or terminal, but with an intuitive UI that allows for efficient iteration, debugging or exploration.