Systems | Development | Analytics | API | Testing

Qlik’s lessons learned from developing systems in a locked-down military-grade data zone at the 2025 NDIA Hackathon In early September, developers from across the country arrive at George Mason University’s Fuse facility with laptops, notebooks, and one big unknown: how do you build a defense-grade analytics solution in just 72 hours in a simulated air-gap environment.

Remote debugging is the most reliable way to grasp what’s really happening inside a mobile app once it’s out in the wild. We’re talking crashes that never appear in development, and issues from users that can’t be reproduced locally. If you ship mobile apps, you’ve been vexed by these problems at some point.

Enterprise development teams face mounting pressure to deliver secure, performant APIs while managing complex distributed architectures, strict compliance requirements, and accelerating release cycles. The API testing platform an organization chooses directly affects product quality, team velocity, and regulatory risk. Functional validation, security testing, performance testing, and CI/CD integration must all scale across global teams without introducing governance gaps.

Your Kafka Doesn't Have to Live Behind a Wall There's a problem that almost every platform team running Kafka at scale eventually hits, and it usually starts with a reasonable ask: "Can you give our partners access to this event stream?" What follows is rarely simple. You start scoping VPC peering. Then someone asks about firewall rules. Then you realize each new external consumer is going to need its own network arrangement.

Whether you’re streaming change data capture (CDC) events from MongoDB to Apache Kafka or sinking high-velocity data from Kafka into MongoDB for analytics, the following best practices ensure a secure, performant, and resilient architecture. This technical deep dive covers implementing the MongoDB Atlas Source and Sink Connectors on Confluent Cloud.

Speed is the heartbeat of DevOps. Teams are delivering faster, integrating continuously, and deploying multiple times a day. But with that velocity comes a question every engineering leader faces: how do you ensure confidence in every release? When change happens this fast, it’s easy to lose track of what’s been tested, what’s passed, and what’s at risk. Without the right visibility, small gaps in testing can turn into production issues that impact users and erode trust.

Enterprise API teams rarely struggle with a lack of tools. They struggle with fragmented toolchains that promise agility but deliver chaos. According to IBM Systems Sciences Institute research, late-stage defects can cost up to ten times more to fix than early detection, while industry analysts report that tool sprawl can waste up to 30% of software expenses through redundant licensing and operational overhead.

Combining MCP Servers and LLMs with MS SQL Server simplifies database management with natural language queries, automation, and robust security. Here’s what you need to know.

In the fast-paced world of mobile engineering, the ability to move quickly is often hampered by the very tools meant to help us. While traditional mobile clouds provide essential scale and coverage, they are often optimized for standard, short-lived regression scripts that follow a predictable path.

Test automation frameworks are shaping how modern teams ship software in 2026. Automation alone is not enough anymore. The structure behind automation decides whether your tests scale or collapse under maintenance pressure. I’ve seen teams struggle when they invest in automation but lack a structured system. Let’s explore how test automation frameworks can make automation reliable and scalable.