Operations | Monitoring | ITSM | DevOps | Cloud

InfluxDB has been a popular time series database for the better part of a decade, and the latest release represents years of work behind the scenes to address several major feature requests users have been asking for since the earliest days of the time series database.

Most monitoring tools weren't built for the AI-first world. By nature, traditional monitoring platforms force you out of your natural coding environment and trap you in clunky web interfaces, brittle configuration panels, and rigid APIs. And sadly, when monitoring providers do offer "AI features," it's usually a chatbot bolted onto their existing UI, being nothing more than a pale imitation of the AI tools you’re reading about every day on Hacker News. All this creates friction.

In this blog series we’ve examined Five Myths of Apache Spark Optimization. But one final, bonus myth remains unaddressed: I’ve done everything I can. The overprovisioned resources that lead to underutilized CPU and memory in my workloads is just the cost of doing business.

Supporting every hospital chart, scan, and bedside alert is a web of digital systems—EHRs, lab interfaces, clinical apps, networks, and connected devices—all working in sync or struggling to. When something slips, say, an Epic interface queue backs up and lab results don’t reach the attending physician on time, the consequences aren’t theoretical. That delay might mean a sepsis alert gets missed. A treatment window closes. A patient’s outcome changes.

As organizations scale their Kubernetes infrastructure, one truth becomes clear: no single team can optimize it alone. Efficiency, resilience, and cost-effectiveness in Kubernetes environments depend on the collective effort of multiple personas, each bringing essential knowledge and responsibility. But it’s not just about division of labor. It’s about active collaboration across roles to unlock the full potential of the platform.

Kubernetes gives you the amazing power to deploy and manage containerized applications. But this power comes with a trade-off. Instead of letting you focus only on writing code and delivering features, Kubernetes also shifts the burden of resource optimization i.e., cost control, performance, and scalability, directly onto your shoulders. The answer to these challenges is automation. Automated optimization takes the guesswork out of resource allocation.

Knowing how to create a database is no longer just a backend task; it’s a core skill for building systems that perform under pressure and scale with complexity. With global data volumes expected to reach 175 zettabytes by 2025, developers must design systems that turn raw information into something meaningful, accessible, and usable. But what does that look like in practice? How do you go from raw data requirements to a reliable schema? Which engine should you choose?