Articles

Ebbs and Flows of DevOps Debugging

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Introduction

Ever since Patrick Debois coined the word DevOps back in 2009, teams and organizations have been clamoring to adopt relevant practices, tools, and a sense of culture in a bid to increase velocity while maintaining stability. However, this race to incorporate “DevOps” in software development practices has resulted in a perversion of the concept. This does not mean that there are no successful practices of teams adopting DevOps practices, but the word overall has become a buzzword. As per the DORA 2019 State of DevOps report, team managers are more likely to proclaim that their teams are practicing DevOps compared to the actual frontline engineers and developers.

Therefore, this piece aims to realign the meaning of DevOps as well as highlight the need for considering debugging as a core element of the practices and cultures that enable DevOps for teams. The argument for debugging as a core component in the DevOps pipeline is a result of the evident need for a shift-left in the way we build and release software, empowering developers to adhere to the intrinsic principle of you build it you run it.

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How to Achieve Effective Kubernetes Cost Optimization

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Kubernetes is ruling the container market. According to a CNCF survey, the use of Kubernetes in production in 2020 was 93%, up from 78% in 2019. Moreover, the survey reveals that the use of containers in production in 2020 was 92%. This figure is up 300% from CNCF’s first survey in 2016. 

Due to the adoption of Kubernetes by DevOps teams and the open source community’s encouragement, this figure could grow more. And if it stays at present prices, this market share still is a significant portion. This means that even though Kubernetes makes a lot of things easier, challenges will always appear, as the survey confirms. Namely, the problems listed include networking, storage, tracking, surveillance, a lack of preparation, and, of course, cost management.

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AWS HIPAA Compliance Best Practices Checklist

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Nowadays, most medical providers across the globe tend to implement cloud-based architecture for their medical services. And it’s not surprising, especially considering today’s pandemic reality; medical software is a must. However, to build a highly secure solution to deliver medical services, you must abide by the US 1996 law, namely the HIPAA Security Rule. This legislation represents a set of required and adequate protections for managing electronic confidential patient information and avoiding its disclosure without prior patient’s knowledge and even consent.

So, if you want to develop a medical solution and make your healthcare services cloud-based, you will have to apply the latest technologies for maintaining data compliance. To build cloud-based apps according to the Privacy Rule, most healthcare providers apply Amazon Web Services (AWS) due to its increased agility, security, and innovation potential.

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Getting Started With AWS Monitoring

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Amazon Web Services (AWS) is the most popular public cloud, with 175 services and counting. A key element of a successful cloud operation is gaining visibility into what is running where, what issues are occurring, and dealing with them, preferably automatically.

In this article, I’ll discuss the basics of AWS monitoring, including Amazon services that can assist with monitoring, key metrics to watch for the most popular Amazon services, and a special focus on monitoring EC2 environments, which are the basis for most Amazon deployments.

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NoSQL Is Dead, Long Live NoSQL

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Dynamo accelerated the NoSQL revolution that’s driving the database industry.

Recently, Amazon announced PartiQL – A SQL-Compatible Query Language for their flagship NoSQL database Amazon DynamoDB. This has brought the NoSQL “re:evolution” full circle. It’s wonderful to see the collaborative research from UCSD and Couchbase enabling the industry to move forward.

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How to Create an AWS Continuous Deployment Pipeline

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Creating a continuous deployment pipeline will bring us a step closer to an automated build, test, deploy strategy. In order to create such a pipeline, we need to have access to several tools. Instead of installing these on on-premise servers, we can make use of the AWS cloud offer. Let’s see how this can be accomplished!

1. Introduction

We want to create an automated pipeline in order to ensure that no manual and error prone steps are required for building, testing and deploying the application. When a failure occurs during one of these steps, we will be automatically notified and can take necessary actions in order to resolve the issue.

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Serverless Visitor Counter Badge with Azure Functions

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We all love web badges. You might have spotted many of them in README of repositories, including the repository of my blog, The Cloud Blog. In general, web badges serve two purposes.

  1. They are visually appealing.
  2. They display key information instantly.

If you scroll to my website’s footer section, you will find GitHub and Netlify badges that display the status of the latest build and deployment. I use them to quickly check whether everything is fine with the world without navigating to their dashboards. In essence, a badge is an SVG image with dynamic content embedded in it.

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Decision Making: Relational or NoSQL

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For some time, products have been leaning towards NoSQL databases because of the number of advantages that they provide compared to Relational databases (RDBMS), especially in today’s distributed systems. There is always a pressure of delivering things faster to make it live to end users. But does that mean relational databases won’t be able to compete with NoSQL databases, provided Relational Databases are still best-known for adhering to the ACID property?

Here, I am going to explore the possibilities from Relational Databases perspective and how they come a long way to compete with NoSQL Databases. I am going to compare two managed databases from AWS, DynamoDB and Aurora, to see if it’s really worth giving credit to one type of database over another.

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DIY Cloud Systems

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Convenience is the key take away from AWS and Azure, because out of the box, they give you all the stuff you need to get started with your application development. However, creating your own cloud doesn’t need to be that difficult. If you create your own cloud you’ll also get rid of dozens of lockin technologies, such as Azure Functions, Lambda Functions, Cosmos DB, etc. These « features » provides you with few necessities besides the usual lockin. An acquaintance of me once compared these features to « the heroin marketing model, where the first doze is always free. »

If you want to create your own cloud, you’ll need the following features to get started.

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Simplifying Terraform Deployments with Ansible – Part 2

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In this series:

I am happy to see that many people are enthusiastic about this series and wish to make their IaC applications better with Ansible. What I intend to do is quite simple. I will write an Ansible playbook that uses the template module (see Templating with Jinja2) and a little magic of Jinja2 templates to load appropriate variables and configurations for each Terraform environment. Finally, I will use the Terraform CLI to deploy and delete the infrastructure.

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