Operations | Monitoring | ITSM | DevOps | Cloud

Effective microservices logging requires standardized formats, centralized storage, correlation IDs, and strong security measures. This guide walks you through setup strategies, best practices, and tool recommendations that won't break your budget.

Effective Node.js monitoring requires tracking runtime metrics (memory, CPU), application metrics (request rates, response times), and business metrics (user actions, conversion rates). This guide covers what to track, how to collect it, and how to set up meaningful alerts.

Amazon Web Services (AWS) powers everything from scrappy startups to Fortune 500 giants. So, AWS interview questions are relevant for all kinds of roles, from infrastructure design to cost governance. Now, while some interviews test definitions and acronyms, the best ones dig deeper. They explore how well you understand AWS trade-offs—think of performance vs. cost, scalability vs. complexity, or security vs. usability.

With advances in AI and heightened compliance requirements, the way we handle data is changing rapidly. This makes a robust database change management process crucial for organizations who still need to ship value to market, while getting the right data into the hands of decision-makers faster. It’s a tough set of demands for any organization to grapple with as they modernize their data for the future.

If you’re a technology reseller in today’s uncertain software virtualization market, you and your customers are probably actively exploring options for virtualized environments. Of course, finding the perfect alternative is easier said than done: it needs to be scalable, easy to migrate to, cost efficient, and provide the same technical capabilities as preexisting virtualization solutions. That’s a seemingly tall order – but luckily, there are a range of options.

Monitoring deployed applications with eBPF is quickly becoming the standard for good reasons, eBPF: Revolutionizing Observability for DevOps and SRE Teams. Not in the least because it allows monitoring to be a purely operations affair, instead of having to instrument each and every application individually. The security-conscious SRE and SRE manager will immediately ask the question: is this secure? And how about this claim that HTTPS traffic can be monitored?

Apache Flink is an open-source, distributed stream processing engine built for real-time, high-throughput data pipelines. It excels at processing continuous data streams with low latency, making it a great fit for use cases like fraud detection, log analytics, real-time dashboards, personalized recommendations, and IoT telemetry.

At CircleCI, we’re committed to making our platform work seamlessly with the AI-powered tools that developers increasingly rely on. Our journey into AI integration is focused on creating a robust Model Context Protocol (MCP) server that allows AI assistants to access and understand CircleCI data in real-time. This enables developers to debug build failures, analyze test results, find and fix flaky tests, and improve pipelines using natural language within their favorite AI tools.