Systems | Development | Analytics | API | Testing

Fivetran usage and pricing made simple — understand MAR (monthly active rows), syncs, and what drives your data costs.

You’re presenting AI-generated analysis in your quarterly strategy meeting. The slides are polished, the insights look solid, and you’re ready to move the conversation forward. Then the CFO leans forward: “Where did this number come from? I reviewed this data last week and something doesn’t add up.”

Healthcare organizations are under growing pressure to connect legacy EHR (Electronic Health Record) and ERP (Enterprise Resource Planning) systems while safeguarding patient privacy and meeting strict compliance standards.

Real-time analytics has become a cornerstone of modern enterprises. Businesses are no longer satisfied with waiting hours or days for insights—they demand answers in seconds. The rise of AI, machine learning, and generative AI has only accelerated this need, putting immense pressure on data platforms to deliver reliable, scalable, and flexible architectures.

It’s Tuesday morning; the data team at a Fortune 500 manufacturing giant receives an urgent request from the sales organization. Customer territories need to be recalibrated based on real-time market dynamics, competitive intelligence requires immediate integration from external sources, and the executive team demands updated revenue projections by Friday's board meeting.

Software teams today face increasing pressure to deliver high-quality applications at speed. Continuous testing, test automation, and traceability are no longer optional — they’re must-haves for scaling development. Tools like Xray provide the structure and visibility teams need, but their success ultimately depends on how effectively people use them. That’s where Xray Academy comes in.

Your CEO just announced the company’s AI-first strategy and the product team is shipping AI features faster than ever. Marketing is promising intelligent automation to customers, while the QA team is left wondering how to actually test this stuff. Every QA team is grappling with the same challenge as AI becomes the default solution for everything from customer service to content generation.

APIs and AI are on a collision course. For decades, APIs have been the foundation of digital reliability: deterministic systems where you send a request, get a predictable response, and trust that what’s defined is what will happen. AI doesn’t play by those rules. Large language models and AI agents operate in probabilities. They don’t just follow contracts; they interpret them. They learn, infer, and sometimes hallucinate.

When managing Docker containers, effective logging is essential for troubleshooting, monitoring, and ensuring compliance. Mismanaged logs can lead to disk space issues, performance bottlenecks, and lost diagnostic data. Here's a quick breakdown of what you need to know: Choose the right logging driver: Options include json-file (default), syslog, journald, fluentd, and none. Each has unique benefits depending on storage, performance, and integration needs.

As a developer, when you think about CI/CD, you probably focus on build times, test results, and deployment pipelines. The infrastructure powering those builds? It's invisible (unless something goes wrong!). At Bitrise, we've spent 10 years refining infrastructure decisions that most developers never see. In this post, we are pulling back the curtain on the infrastructure choices we've made and why they matter for reliability, consistency, and performance.