Systems | Development | Analytics | API | Testing

In today’s enterprise environment, digital transformation no longer lives solely in the domain of IT. Digital architects, digital technology teams, and cross-functional product leaders now carry critical responsibilities for building digital experiences, embedding AI, and delivering innovation on tight deadlines. Yet far too often, these teams are held hostage by a legacy dynamic: IT owns the data.

The software and product life cycle is fraught with pitfalls and tradeoffs. While testing applications under production-like load is critical to ensuring the reliability, performance, and security of your data storage and software services, you need to do this testing without actually affecting the production data and systems. In essence, you have to pull off the impossible – be as close to production as you can without actually being production.

The longstanding Common Vulnerability and Exposure (CVE) database has vitally guided security teams for over 20 years, connecting cybersecurity experts, developers, vendors, and researchers in their shared ability to track unknown vulnerabilities in software. But in April of 2025, the MITRE CVE database program was in jeopardy. U.S. government funding for CVE, managed by MITRE and sponsored by CISA, was set to expire. Only in the 11th hour was funding secured, and the contract extended — for now.

Dev teams often face delays of days or even weeks waiting for database refreshes. The result? Blocked sprint deadlines and delayed releases. Traditional database provisioning methods often create bottlenecks in DevOps pipelines where speed matters most. But there is a solution to this problem: database cloning. It provides fast, space-efficient copies that speed up development velocity.

At the 2025 instalment of its WWDC event, Apple set out its long-term vision for how we think about platform strategy, AI integration and multi-device architecture. If you’re a CTO, staff engineer, or mobile lead, this wasn’t just a conference to watch, it was one to plan your entire roadmap around. What Apple revealed at this year’s WWDS will affect everything from your frontend stack to how your systems talk to hardware.

As artificial intelligence (AI) reshapes industries, it’s quietly revolutionizing the heart of IT: The data center. The explosive growth of AI workloads is driving up power usage, challenging cooling systems, and demanding a fundamental rethink of how we store and move data. In this new landscape, flash storage stands out – delivering the performance, efficiency, and scalability that AI needs to truly accelerate.

Imagine your development team just released a new feature to collect user preferences. Within hours, a data protection complaint from the EU lands on your legal team’s desk. The user claims they can’t delete their account—and worse, their data is being shared without consent. This isn’t a rare occurrence in today’s data-rich world. When GDPR compliance breaks, it’s not just about fines; it’s also about damaged reputation and lost customer trust.

Operational and analytical estates have been separated since data warehouses were first introduced in the 1990s. The operational estate includes microservices, software-as-a-service (SaaS) apps, and enterprise resource planning systems (ERPs) that have become the beating heart of an organization. The analytical estate consists of the data warehouses, lakehouses, artificial intelligence (AI)/machine learning (ML) platforms, and other custom batch workloads that support business analysis and reporting.

Filtering events in REST APIs lets you request only the data you need, improving efficiency, reducing server load, and speeding up responses. The process involves using query parameters and operators to define conditions for retrieving specific records, like filtering by date, category, or status. Here's the core idea: Query Parameters: Add key-value pairs to the URL (e.g., ?date=2022-03-01) to filter events by specific fields.

Regression test cases are a core part of any stable release cycle. They help you confirm that what used to work still works, even after new features are added or bug fixes are applied. But not every regression test case should be automated. Some are too brittle. Some don’t run often enough. Others are simply not worth the maintenance effort. So how do you decide which ones are worth automating? This article will guide you through exactly that.