Systems | Development | Analytics | API | Testing

One of the busiest engineers at Bitrise doesn't have a laptop. It lives in Slack. Assign it a Jira ticket or @mention it in a thread, and it reads the requirements, posts a plan, spins up a cloud dev machine, writes and tests the code, opens a pull request, watches CI, reacts to review comments as they land and once the pull request merges, cleans up after itself and closes the ticket.

Most enterprises are spending on AI faster than they’re becoming ready to run it. Over the next two years, IT leaders plan to grow their AI investment by 70%, according to our 2025 State of Data Infrastructure Global Report. The capability keeps arriving. The readiness to operate it does not keep pace.

AI is becoming part of every stage of the testing lifecycle. Teams are using it to analyze requirements, design test cases, generate automation scripts, and accelerate execution activities that previously required significant manual effort. Within Xray, AI already helps transform Jira requirements into actionable test cases with AI Test Case Generation.

Running Apache Kafka on Amazon Managed Streaming for Apache Kafka (MSK) gives you a managed broker with no ZooKeeper to operate, automated patching, and multi-AZ replication handled by the service. What it doesn't give you is a safe, governed way to expose Kafka access beyond your VPC boundary. That problem looks simple on the surface. It isn't. And how you solve it has significant implications for security posture, operational complexity, and monthly cost.

Fivetran helps you turn unstructured data into rich context in several ways.

For more than a decade, I have argued that the most valuable skill in a data-driven organisation is not access to information. It is the judgment to know when that information is wrong. AI has made that skill more important, not less. When I wrote about AI literacy in 2023, the pushback I heard most often was that the technology was not yet good enough for the question to matter. Now it is. AI can generate answers, summaries, recommendations, code, analysis, and increasingly, actions.

Every year, retailers invest millions in uptime. Yet when disaster strikes, most discover their DR plan protects infrastructure, not the business that runs on it. “It wasn’t the outage that hurt us most. It was the 45 minutes we had no idea it was happening.” – CTO, UK fashion retailer, post-incident review You have a DR plan. Your infrastructure team tested it six months ago. Your board presentation has a green tick next to Business Continuity.

Picture this: you’ve completed a round of performance testing, and your load testing scripts all pass with flying colors. The release goes live, but within hours, users report sluggish performance and intermittent failures. What happened? In most cases, traditional scripts relied on static payloads and linear request patterns, failing to reflect the unpredictable, multi-step journeys of actual users.

For years, the "Data-Driven" dream has looked a lot like a crowded screen. We built dashboards for every department, every KPI, and every niche project. But as we reached "peak dashboard," a frustrating reality set in: we were drowning in visualizations but starving for immediate insights.