Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

Taking a look back over the last ten years in enterprise technology, paradigm shifts are occurring more frequently. For example, the maturity of DevOps/Platform Engineering and Cloud Native infrastructure has occurred. The new frontier depending where you are in adoption is AI. As your adoption and maturity curve progress, operationalizing these paradigms become important.

AI cost management is the practice of tracking, allocating, and optimizing the cloud infrastructure costs tied to building, running, and scaling AI workloads. It differs from traditional cloud cost optimization because AI infrastructure behaves differently at every layer of the stack. The biggest problem isn’t overspending. It’s that most organizations can’t see where their AI spending is going.

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.