Introduction As the world continues to embrace the digital revolution, Application Programming Interfaces (APIs) have become instrumental in enabling seamless interaction between different software applications. APIs enable developers to harness the functionalities of other software applications without necessarily understanding their internal workings. However, this does not mean that APIs can be used recklessly. APIs are…
The celebrated mantra “fail fast, fail forward” is often proudly declared by many a tech startup. This philosophy, though steeped in pragmatism, encourages entrepreneurs to experiment, learn from mistakes swiftly, and pivot as needed. However, within the ever-evolving tech ecosystem, this doctrine, when misapplied, can lead to an insidious and crippling problem — technical debt.
Technical debt — the term coined by software developer Ward Cunningham — refers to the eventual consequences of poor system design, software architecture, or software development within a codebase. In the race to fail fast and push ahead, many startups fall into the trap of cutting corners, creating a ticking time bomb that grows progressively destructive over time.
Every production environment is a complex ecosystem, a living, breathing entity. The more we understand the interconnectivity and nuances of this environment, the better we can optimize it, troubleshoot issues, and increase overall performance. But how do we gain such deep insights? Enter the concept of observability.
MongoDB, an industry heavyweight in the NoSQL database arena, is adept at managing extensive datasets. However, as with any powerful tool, MongoDB’s efficiency and effectiveness are contingent on proper utilisation.
The need for thorough and precise quality assurance in software development cannot be overstated. It ensures the robustness, reliability, and user-friendliness of the application. Selenium-based QA Sanity Test Suites are well-known for their efficiency in automating browsers for testing web applications. However, they are also CPU-intensive and, when run locally, they may fail, consume substantial resources, and take up a lot of time. Not only that, local executions lack centralized housekeeping and uniformity in compute resources. Moreover, they present challenges in scalability.
Welcome to Part III of our blog series on migrating stateless Node.js API workloads from Amazon EC2 to Amazon EKS. In Part II, we set up the EKS environment, Dockerized our Node.js application, prepared Kubernetes manifests and Helm charts, and fine-tuned resource limits. Now, it’s time to execute the actual transition. In this phase, we will guide you through choosing the right time for migration, initiating the phased transition, monitoring the new setup, addressing any issues, deprecating the old EC2-based service, and emphasizing continuous monitoring and improvement.
Welcome to the second part of our series on transitioning stateless Node.js API workloads from Amazon EC2 to Amazon EKS. Here, we’ll delve into the details of setting up your EKS environment, dockerizing your Node.js application, and preparing Kubernetes configurations. Bear in mind that this guide is explicitly tailored for stateless workloads and doesn’t delve…
Transitioning from a familiar environment to a new one is a substantial task that demands careful forethought and strategic planning. Part I of our series on moving stateless Node.js API workloads from Amazon EC2 to Amazon EKS spotlights the importance of understanding your existing setup, assessing dependencies, and crafting a comprehensive migration plan.
Please remember that this guide is specifically tailored to stateless workloads and does not delve into issues related to databases or stateful services.
In the current cloud-native era, Kubernetes has rapidly emerged as a favored choice due to its efficiency, scalability, and robust orchestration capabilities. Numerous organizations are shifting their workloads from traditional virtual machine environments to container orchestration platforms, such as Amazon’s Elastic Kubernetes Service (EKS).
In an increasingly digital world, the preservation and availability of data is of paramount importance. Businesses and organizations rely on datacenters to store, process, and manage their vast amounts of data. However, these datacenters are not invincible; they are susceptible to a multitude of disasters, both natural and man-made. This is where the concept of disaster recovery comes into play.
Disaster recovery (DR) encompasses the policies, tools, and procedures that are implemented to enable the recovery and continuation of vital technology infrastructure and systems following a natural or human-induced disaster. The primary goal of disaster recovery is to minimize downtime and data loss – two variables that can severely impact a business’s operations and reputation.