Operations | Monitoring | ITSM | DevOps | Cloud

Before I worked in tech, I worked in logistics. I saw a specific pattern repeat itself at office supply companies over and over, until I could see it coming before the customer did. The pattern went like this. A small office supply company would sell paper and pens to local businesses. One day a customer asked, "can you deliver a box of paper?" The salesperson said yes, drove the box over in their car after work, and thought nothing of it. The customer told their friend.

One of the most counterintuitive failure modes in enterprise ActiveMQ deployments is this: a single application team deploys a new consumer for a high-volume market data topic. Their consumer is slow, maybe they added a database write on every message, or their processing thread pool is undersized.

‍Summary: Google Cloud Next ’26 focused on the future of software delivery, emphasizing that AI, platform consolidation, and an urgent push toward efficiency are reshaping the Software Development Life Cycle (SDLC). The key takeaway from the event was that organizations are moving from AI experimentation to operationalization, actively consolidating fragmented tools onto end-to-end platforms that embed AI for control, intelligence, and speed. ‍

It’s becoming increasingly clear that AI-generated code can create real challenges once it reaches production. At Harness, we’ve been focused on innovating fast and solving those problems, so teams can move quickly without sacrificing reliability. In the past 30 days, we delivered 70+ new features.

Security and platform engineering teams rely on context-rich logs to investigate threats, prioritize incidents, and meet compliance requirements. Context is often stored separately from applications that generate logs, in sources like threat intelligence feeds in Snowflake, asset lists in Amazon S3, ownership data in ServiceNow CMDB, and risk scores produced in Databricks.

A procurement officer at a mid-sized financial services firm spent six months last year negotiating with a cloud provider that turned out not to hold the certification it had implied in its sales deck. The contract collapsed during legal review. The firm lost the time, the provider lost the deal, and somewhere in the middle, a senior engineer learned the difference between "compliant with the principles of" and "audited to the standard of.".

Recurring incidents are a symptom of a broken process. Your teams are working hard to get services back online, but constantly battling the same problems is frustrating and not a sustainable approach. What’s reflected here is not a failure in engineering abilities, but a deficiency in the learning that should follow an incident. When incident analysis focuses on finding a single person or team to blame, it creates a culture of fear.

Criteo handles 23 million requests per second (RPS) while maintaining peak performance and minimizing downtime. For most organizations, handling that level of traffic is just a theoretical stress test — a what-if scenario should their infrastructure ever be overwhelmed by an unexpected wave of requests. But for Criteo, 23 million RPS is just another Tuesday.

This straightforward guide will help you understand GCP storage pricing without the jargon. Understanding where your cloud spend goes enables you to pinpoint who, why, and what drives your cloud costs. This visibility supports informed decisions about reducing unnecessary spend or increasing investment in high-return areas.