Systems | Development | Analytics | API | Testing

The phrase “Big Data” may be out of trend, but data volumes keep climbing–and so do expectations. It’s estimated that in 2026, the global volume of data is expected to exceed 221 zettabytes. With AI tools and agents making it easier to consume, the pressure is on to deliver faster, more responsive insights on massive datasets to more users than ever.

AI isn’t something we’re waiting for anymore. It’s already here. Every time you check directions, talk to your phone instead of typing, or unlock your device with your face, AI is doing the work in the background. The same shift is happening inside organizations. Companies and government agencies are using AI to answer questions faster, cut down manual tasks, support their teams, and improve how they serve people. But the real question is, Are U.S.

Modern development teams need testing infrastructure that keeps pace with release schedules. Here's how the leading cloud testing platforms compare on coverage, reliability, and real-world performance.

YAML is often the better choice for OpenAPI specifications due to its readability and ease of manual editing. It uses indentation instead of braces and brackets, supports inline comments with #, and handles multi-line text more naturally. This makes YAML easier to maintain and understand, especially for teams collaborating on API documentation. However, JSON has its strengths too. Its strict syntax ensures precision, making it ideal for automated workflows and machine-driven processes where speed matters.

Creating a RESTful API from scratch can be a tedious task, especially when you're juggling tight deadlines or multiple projects. That's where a tool like our REST API Generator comes in handy. It takes the grunt work out of building endpoints by automating the process, letting developers focus on what matters—crafting great applications. Simply provide your database schema or a JSON outline, select a framework, and watch as fully functional CRUD operations are created for you.

Today, software is produced at lightning speed, but speed without quality can create chaos in production. That’s why high-performing teams rely on DORA metrics to assess the speed and efficiency at which they are delivering their changes, while still being able to maintain a stable environment. The DORA metrics can allow engineers to take their software delivery process and convert it from an "on-demand" to a "data-driven" model.

Monitoring microservices is challenging but essential for maintaining system performance and reliability. Unlike traditional applications, microservices require tracking individual services, their interactions, and the infrastructure they run on. Here's what you need to know: To succeed, instrument your services early, set clear Service Level Objectives (SLOs), and ensure your tools scale with your architecture.

Here's a statistic that should concern every CFO: according to 2024 research from Accenture, finance teams spend 85% of their time on data triage-gathering, validating, and reconciling numbers. Only 15% of their time goes to the strategic work they were actually hired to do. If that sounds familiar, you're not alone. The reality is that most CFOs today can't confidently answer a deceptively simple question: "Where did this number come from?".

A product analyst helps teams understand how users interact with a product — and turns that data into decisions that improve growth, retention, and user experience. They sit at the intersection of: Instead of guessing what users want, product analysts rely on behavioral data to guide decisions.

Remember when Jarvis ran Tony Stark’s entire life and helped him build a high-tech Iron Man suit? That’s pretty much AI in eCommerce now, but instead of building suits, it’s building smarter stores and smoother shopping experiences. While most people are still stuck using AI to write basic product descriptions or churn out ad copy, the real power of artificial intelligence in commerce lies under the hood.