Systems | Development | Analytics | API | Testing

Software ships faster than ever, but speed introduces risk without the right crash monitoring and error reporting tools providing visibility into what happened, why, and how to fix it.

‍Mobile development has a reputation for being slow, complex, and harder than it needs to be. Platform quirks, rigid review gates, and ever-growing app complexity can make it feel like the toolchain is working against you. But the data tells a different story. We analyzed tens of millions of builds across thousands of mobile teams on Bitrise, spanning three years of real-world data from 2022 to 2025. The results challenge some common assumptions, and confirm others.

The appetite for real-time data continues to grow. Across industries, the ability to act on data as it arrives is increasingly central to how leading organizations compete, from IoT and fraud detection to event driven analytics and AI agent architectures. Streaming data is no longer a specialist workload. It is becoming a core requirement. I am excited to announce that streaming ingestion is generally available in Qlik Open Lakehouse, part of Qlik Talend Cloud.

For fifty years, the hardest part of software was writing it. That's no longer true. In 2025, AI coding assistants went mainstream — 90% of developers now use them (DORA 2025). Then came background agents: autonomous systems that take a ticket, write the code, run the tests, and open a pull request while the engineer sleeps. Stripe merges over 1,000 AI-written PRs per week. Ramp reached 30% AI-authored PRs within two months. Spotify has merged 1,500+ agent-generated PRs into production.

Production bugs that only reproduce in actual traffic can be some of the most frustrating bugs in software development. You can stare at your logs, add traces to your code, add instrumentation – and still not be able to see the actual requests that went over the wire. And that gets even harder when the requests are encrypted and the system is a black box. You can use tools like Wireshark or Kubeshark to capture the requests.

Enterprise Spring Boot APIs should be tested at three levels: unit tests for business logic, integration tests for external service behavior, and traffic replay for production edge cases. Most teams only do the first. This guide shows all three using a real Spring Boot application that calls external APIs (SpaceX, US Treasury) with JWT authentication. The kind of service that looks simple in development and breaks in production.

Learn what reverse ETL is and how it enables operational analytics. Explore its process and applications within sales, marketing, and support.

Author: Adam Wolf Efficient resource allocation is a foundational requirement for scaling AI workloads, particularly as organizations move from isolated experiments to shared infrastructure supporting multiple teams, models, and environments. GPUs, CPUs, and high-performance storage are costly and finite, and without coordination, utilization often degrades as usage grows.

AI isn’t just some side project anymore. These days, it’s a real budget line for big companies, something boards talk about all the time. Global investment in AI is about to break $300 billion a year. McKinsey says AI could add up to $4.4 trillion to the economy every year. That’s huge. But even with all this promise, a lot of businesses still have trouble figuring out if their AI projects are actually paying off. That’s the spot most CXOs are stuck in now.

Modern software architectures have rendered traditional QA obsolete. In an era of distributed microservices and serverless functions, bugs are no longer just code errors; they are systemic interaction failures. While Agile successfully accelerated delivery, it left a critical gap in quality assurance. The industry's initial response, splitting focus between "Shift Left" and "Shift Right", created a fragmented safety net.