Operations | Monitoring | ITSM | DevOps | Cloud

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.

As a team, we have spent many years troubleshooting performance problems in production systems. Applications have become so complex that you need a standard methodology to understand performance. Our approach to this problem is called the Golden Signals. By measuring these signals and paying very close attention to these four key metrics, providers can simplify even the most complex systems into an understandable corpus of services and systems.

Update (March 2026): AWS has announced that AWS Proton will reach end of life on October 7, 2026. After that date, the Proton console, API, and pipeline management will be permanently unavailable. If you're currently using Proton, read our migration guide.

AWS Proton is reaching end of life. If you're reading this, you probably just found out — either from the AWS console banner, your account manager, or a panicked Slack message from someone on your platform team. Here's what you need to know: your infrastructure is safe, but the tool you use to manage it is going away. You have until October 7, 2026 to find a replacement. That sounds like plenty of time. It isn't.

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.

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.

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.