Operations | Monitoring | ITSM | DevOps | Cloud

In modern IT environments, data is abundant, but clarity is rare. Enterprises deploy dozens of monitoring tools to collect metrics, events, and logs from across the network, yet when something goes wrong, teams still scramble to connect the dots. Why? Because these data streams exist in siloes, isolated by format, source, or system.

With the introduction of Aiven for AlloyDB Omni, many of our customers have wondered what the difference is between AlloyDB Omni and its architectural source project, open source PostgreSQL. AlloyDB Omni is more than just a re-implementation of PostgreSQL, and understanding how it is similar – and how it differs – from open source PostgreSQL is a valuable exercise.

Time flies, and just like that, we are already in April! The first quarter of 2025 has been packed with exciting updates for VictoriaMetrics Cloud. If you joined our latest Quarterly Virtual Meetup, you might have already seen some of these announcements alongside other great improvements across all things VictoriaMetrics. In this post, we’ll take a closer look at what’s new in VictoriaMetrics Cloud.

Prometheus Monitoring: Instant Queries and Range Queries Explained This discussion is part of the basic monitoring series, an effort to clarify monitoring concepts for both beginners and experienced users.

Every business strives to optimize operational costs and efficiency. In the DevOps world, where cloud-scale operations are the norm, this becomes even more critical. At JFrog, while delivering a robust and highly scalable SaaS solution to our customers, we are equally focused on optimizing operational costs and maximizing infrastructure efficiency.

In fast‑moving product and support teams, the next unanswered question is never far away.

As much as you try to prevent it, your product will break sometimes. While you hope it would have the decency to do so while you are awake and already working, sometimes the product is inconsiderate and decides to break outside your office hours. Being woken up from a page at 3 am sucks, and being woken up again two hours later (when you get pinged for a follow-up issue you missed the first time) sucks even more.

Customer service has gone through a fundamental transformation in the last few years. What started as reactive support through call centers has evolved into AI-driven interactions designed for speed, efficiency, and personalization. According to McKinsey, integrating generative AI into customer care functions can drive productivity gains of 30-45%. Agentic AI is taking this further by automating entire workflows.

You’re staring at logs, trying to figure out what caused that odd error in the middle of the night. Or maybe you're following a chain of requests across services, hoping to understand how one user action triggered a series of unexpected behaviors. That’s where distributed tracing and request tracking—specifically, correlation IDs and trace IDs—are invaluable. It’s the kind of detail that can make debugging faster and less painful.

Modern systems throw off a lot of data—metrics, traces, logs—sometimes more than we know what to do with. When you're trying to understand how values spread out over time (like response times, memory usage, or queue lengths), averages alone don’t tell the full story. OpenTelemetry histograms help fill in those gaps. This guide walks through what they are, why they matter, and how DevOps engineers can use them to improve observability in real systems.