Operations | Monitoring | ITSM | DevOps | Cloud

Summary: Today, many teams are moving from MySQL to PostgreSQL as they update their database systems and plan for future growth. However, too often, there is extra work after the migration: for example checking that tables and constraints were copied correctly, tuning performance, and confirming that replication works properly. Devart’s PostgreSQL tools help DBAs with these tasks through features like Schema Compare, Data Compare, and other tools that help review and manage PostgreSQL databases.

Short Summary: PostgreSQL can slow down when range queries and frequent data updates rely on the same indexes. This guide shows how to spot the problem and use Devart tools to reduce B-Tree index conflicts, improve query plans, and manage bi-weekly data updates in AWS Aurora.

It’s an all too common scenario: You get paged for some queries timing out, but when you investigate, the database performance looks unchanged. Something must have changed, though. If the database doesn’t look overloaded, where are these timeouts coming from? The answer often lies outside the database itself. Round trip query latency includes every hop between your application and the database, including connection pools, load balancers, and proxies.

The latest VirtualMetric DataStream release brings a significant step forward in platform observability and deployment flexibility. Version 1.9.0 gives security and infrastructure teams direct visibility into what’s happening across their pipelines in real time while expanding support for cloud-native environments and broadening connectivity options. Here’s what’s new.

A complete guide to modern incident management and how it’s transforming into a strategic business function. Kamalesh Srikanth , Product Strategy Leader at AlertOps If you’ve worked in IT, infrastructure, or operations for any length of time, you’ve lived through the chaos of a critical incident. Systems down, alerts blaring, Slack pinging, emails piling up and somewhere in that noise, your team is trying to figure out what actually broke and how to fix it fast.

This comprehensive guide covers the fundamentals of load testing, key differences from stress and performance testing, step-by-step execution methods, popular tools, and best practices to help teams build resilient systems with confidence. In today's always-on digital economy, a single slow page or unexpected crash during peak traffic can cost businesses thousands or even millions of dollars in lost revenue, damaged reputation, and frustrated customers.

AI coding agents like Claude Code and Codex are becoming a real part of developer workflows. They don't just write code, they call APIs, interpret responses, and take action based on what they find. That means the quality of your API responses directly affects how useful an agent can be. We've shipped a series of improvements to the Oh Dear API with this in mind. Every change helps humans too, but we specifically optimized for how agents consume and reason about data.

It’s 2:47 a.m. A P1 alert fires. The on-call engineer opens ilert, sees the AI has already investigated, and is presented with three remediation options. What happens next is the moment we obsessed over. ‍ Most AI tooling at that moment hands the engineer a numbered list in a chat window and waits. The engineer reads, selects mentally, types a reply, and the agent resumes.

In the modern DevSecOps world, CISOs are constantly looking for signals in the noise, and the outputs of security scanners often carry a lot of weight. A security scan that returns a “zero CVE” report often unlocks promotion to production; a single red flag can block a release. This binary view of security has birthed two diametrically opposed philosophies. On one side, we have the long-term support (LTS) approach: stay on a battle-tested version and backport specific security fixes.

I’ve been an engineering leader for over a decade, and I’ve spent most of those years in private Slack groups with other engineering leaders, comparing strategies and kvetching about Kubernetes. Of the hundreds of threads I’ve taken part in, the one that got the most engagement the fastest was a recent one around AI adoption. “Where are you on this continuum?”, it read. “A. You don’t really care how people use AI; B. You push people to use AI; or C.