Operations | Monitoring | ITSM | DevOps | Cloud

In backend engineering, many days follow a familiar rhythm: coffee, code reviews, maybe deploying a new feature. But occasionally, the routine is interrupted by a message that signals a different kind of challenge, like a Slack notification from the security team: “Hey, we’ve identified a potential issue. Need to sync up.” This post details one such instance—our journey addressing CVE-2024-21626, a privilege escalation vulnerability reported in Rancher.

The scarcity of labor resources and high costs of robotics ownership are pushing fulfillment companies to rethink their automation strategies. Zebra addressed these challenges head on in a recent Making the Case report published in collaboration with Modern Materials Handling. This report delves into the top challenges fulfillment operations are dealing with on the robotics front today.

When SentinelOne, a leader in cybersecurity and endpoint protection, experienced a major outage last week, thousands of organizations were suddenly left in the dark. With SentinelOne down for hours, IT and security teams scrambled for information and updates. But there was a critical missing piece: SentinelOne has no public status page. This gap left customers frustrated, searching for answers on social media, Reddit, and unofficial channels.

Technology leaders often face a tempting proposition from their engineering teams: “We could build this ourselves.” It’s a natural instinct, especially when discussing incident management systems. Your team’s confidence isn’t misplaced – they absolutely could build a basic alerting system. However, the question isn’t about capability; it’s about strategic resource allocation and long-term operational excellence.

Traditional databases store data in structured tables, whereas NoSQL (non-SQL) databases use more flexible, non-tabular storage methods. NoSQL databases can store a wider range of data types, including document stores, wide columns, key-value stores, and graphs. These databases first emerged in the late 2000s to support massive horizontal scaling and high-throughput workloads for web applications.

Coroot is excited to feature an editorial from GlassFlow for our first Open Source Spotlight. We hope to improve the workflow of our global community of SREs and DevOps professionals by sharing exciting projects like Glassflow, which make innovation accessible for everyone through the freedom of open source. If you have an open source or open core project you’d like to see on our blog next, send us a message!

Splunk recently announced exciting updates and significant modernizations for the upcoming releases of Splunk Enterprise and Splunk Cloud Platform. This blog is the first in a series to help prepare your organization for these changes by exploring upgrade readiness best practices. This first installment will highlight the Splunk Health Assistant Add-On, a vital tool that supplements the Splunk Enterprise Monitoring Console, designed to streamline your transition to the next version of Splunk Enterprise.

Distributed tracing tracks requests as they flow through microservices, revealing bottlenecks, failures, and performance patterns. Without proper tracing, debugging production issues becomes guesswork—especially in complex architectures with dozens of services. Modern applications generate millions of traces daily. The right vendor helps you extract actionable insights without drowning in data or breaking your budget.

Last week saw several huge milestones for BigPanda. We launched the BigPanda agentic IT operations platform, a sweeping evolution of our product offerings. As part of this launch, we also introduced two new AI solutions, BigPanda AI Detection and Response and BigPanda AI Incident Assistant. These powerful new capabilities bring agentic AI into IT operations, transforming how enterprises automate the manual and time-intensive workflows of ITOps, L1 response, and incident management.

Datadog may be the default choice for all-in-one observability, but its logging experience takes a back seat to the broader platform. Logs are primarily designed to feed into metrics and traces, which leads to tradeoffs such as slower search, complex workflows, and a UI that isn’t optimized for log investigations. As a result, Datadog doesn’t align with how developers actually troubleshoot.