Microservices and Kubernetes have completely changed the way we reason about network security. Luckily, Kubernetes network security policies (KNP) are a native mechanism to address this issue at the correct level of abstraction. Implementing a network policy is challenging, as developers and ops need to work together to define proper rules. However, the best approach is to adopt a zero trust framework for network security using Kubernetes native controls.
In this article, we’ll cover the key differences between APIs and microservices as answered by our contributors consisting of senior decision-makers and CTOs from technology companies around the world. One of the most popular ways to consume data from a web service is through a web application programming interface (API). By interface, we are referring to an agreement, or schema, that anyone using this API must abide by.
On November 11, 2020, I had the pleasure of speaking with two incredible thought leaders in the DevOps space: Vivek Pandey, VP of Engineering at Shipa (https://www.shipa.io), and Patrick Deuley, Sr Product Manager at GitLab (https://www.gitlab.com). The topic of the discussion was “Monolith to Microservices,” and we covered three key areas: Starting out, Scaling and Developer Experience.
Kubernetes is increasingly becoming a uniform standard for computing – in Edge, in core and in the cloud. At NTS, we recognize this trend and have been systematically building up competencies for this core technology since 2018. As a technically-oriented business, we regularly validate different Kubernetes platforms and we share the view of many analysts (e.g. Forrester or Gartner and Gartner Hype Cycle Reports) that Rancher Labs ranks among the leading players in this sector.
The network is foundational to distributed application environments. A distributed application has multiple microservices, each running in a set of pods often located on different nodes. Problem areas in a distributed application can be in network layer connectivity (think network flow logs), or application resources unavailability (think metrics), or component unavailability (think tracing).
If you are dealing with microservices, serverless architecture, on any other type of distributed architecture, you have probably heard the term “Distributed Tracing.” You may have been wondering what it’s all about, and where should you start, in this post, I’ll tell you about the journey we passed at Duda, from the day we heard about distributed tracing and started to explore whether it will be useful to use it in our company, to the exploration on what is distributed tracing a
Your team has decided to migrate your monolithic application to a microservices architecture. You’ve modularized your business logic, containerized your codebase, allowed your developers to do polyglot programming, replaced function calls with API calls, built a Kubernetes environment, and fine-tuned your deployment strategy. But soon after hitting deploy, you start noticing problems.
Trends in the infrastructure and software space have changed the way we build and run software. As a result, we have started treating our infrastructure as code, which has helped us lower costs and get our products to market more quickly. These new architectures also give us the ability to test our software faster in production-like deployments, and generally deliver more stable and reproducible deployments.
