Systems | Development | Analytics | API | Testing

You’ve probably heard of the concept of ‘Frontend decides, backend delivers’ in app development. On the off-chance that you haven’t, it means that the frontend defines the data it needs, and the backend acts on this instruction. This makes the data-fetching process more efficient, simplifies the error handling process and frees us, the devs, from the need to constantly make backend changes. The GraphQL query language for APIs, developed by Facebook, is a vital tool in this regard.

Common Vulnerability Scoring System (CVSS) and the National Vulnerability Database (NVD database) help you to properly assess which software vulnerabilities should be your top priority. Here, we explain what is the National Vulnerability Database (NVD), what is the Common Vulnerability Scoring System, and how CVSS is used to calculate risk. Read along or jump to the section that interests you the most.

In this post, we'll run through testing a Node-MongoDB app, step by step. You can test MongoDB using mongodb-memory-server, an in-memory version of MongoDB that runs independently of a persistent database. A freshly spun-up mongod process starts at roughly 7 MB of memory, providing a lightweight, self-contained environment for running tests. Let's get going!

Our previous article in this series established that Solid Queue is an excellent choice if you need a system for processing background jobs. It minimizes external dependencies — no need for Redis! — by storing all jobs in your database. Despite that, it is incredibly performant. But just being performant is not enough for a production-ready background job system. Rails developers have come to expect a lot over the years. We don't just want to enqueue jobs to run in the background.

Struggling with data consistency in sharded APIs? Here's what you need to know upfront: Data sharing improves performance by dividing data across multiple databases, but it introduces challenges in maintaining consistency. Consistency models matter: Choose between strong consistency (immediate accuracy, higher latency) and eventual consistency (temporary inaccuracies, higher performance).

AI is a groundbreaking technology that is ready to modernize the way federal government agencies operate. By automating tasks and optimizing workflows, artificial intelligence (AI) promises to enhance efficiency, minimize errors, and boost productivity without adding resources. But as with any change—and especially one as transformative as AI—leaders need to take deliberate and cautious steps to ensure a smooth integration of these innovations and to gain the buy-in of government employees.

To be good stewards of taxpayer dollars, state and local governments conduct market research and perform due diligence before purchasing a software solution. Commercial off-the-shelf (COTS) products are often positioned as offering the best price tag and the fastest deployment. However, the promise of a speedy installation often goes unmet.

In this episode, Emmanuel Paraskakis, CEO Level 250 breaks down the core responsibilities of an API product manager. Speaking from his experience in product management for over fifteen years, Emmanuel distinguishes an API product manager’s focus from conventional product roles, underscoring their critical importance in building scalable digital platforms.

NGINX One provides a modern solution for enterprises to manage infrastructure at scale across globally distributed systems. The platform has built-in tools for essential performance and uptime metrics, giving DevOps teams visibility into the health of their NGINX instances. But for effective API observability and analytics, you have to go beyond infrastructure metrics.

API versioning is essential for managing changes without breaking your system. In microservices, where APIs are the backbone of communication, versioning ensures stability, backward compatibility, and smooth updates. Here’s a quick breakdown of what you need to know.