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The latest News and Information on DevOps, CI/CD, Automation and related technologies.

Release v2.1: Performance & Scalability Improvements, Configurable Alert Repeat Notifications & more

Dec 23, 2024 By netdata In netdata
The Netdata Team is very excited to introduce you to Netdata v2.1 and to all the new features and improvements in the new version. RELEASE HIGHLIGHTS: Major Performance and Scalability Improvements This release significantly enhances Netdata's performance and streaming capabilities, with particular focus on multi-parent infrastructures: Cloud: Automated Room Assignment with Label-Based Rules Netdata Cloud Dashboard introduces node membership rules—a powerful new feature that transforms how you organize your infrastructure monitoring.
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speedscale

Testing Kubernetes Ingress with Production Traffic

Dec 22, 2024 By Kush Mansingh In Speedscale
Kubernetes is an incredibly powerful solution, but testing the Kubernetes Ingress resources themselves can prove to be quite tricky. This can lead to significant frustration for developers - bugs can pop up in production that weren't caught during testing, workflows that make sense on paper might fail in practice, and so forth.
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squadcast

Incident Management Beyond Alerting: Utilizing Data & Automation for Continuous Improvement

Dec 20, 2024 By Vishal Padghan In Squadcast
Managing incidents effectively is not just about responding to alerts; it’s about building a resilient system that thrives on continuous improvement. Modern organizations operate in complex environments where even minor disruptions can escalate into major issues. This calls for a proactive approach that leverages data and automation to optimize the entire incident response lifecycle.
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manageengine

Decoding devices with DHCP fingerprinting for smart IP address assignment

Dec 20, 2024 By DDI Central In ManageEngine
In today’s dynamic network environments, where countless devices—ranging from laptops and smartphones to IoT sensors and smart appliances—connect and communicate, efficient IP address management is critical. Ensuring each device receives the right configuration not only optimizes network performance but also improves visibility and control. However, identifying these devices accurately can be challenging, given the diversity of operating systems, hardware, and vendors.
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speedscale

A Guide to Optimizing Kubernetes Clusters with Karpenter

Dec 20, 2024 By Kush Mansingh In Speedscale
With the promise of auto-provisioning and self-healing, Kubernetes environments can be an attractive option for hosting your application platform. However, with increasing budget restrictions, the competitive cloud providers and offerings, and the need to do more with less, engineers are looking to get a handle on their resource utilization.
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cloudzero

Your Guide To Datadog Cost Optimization: 7 Tips For Reducing Spend

Dec 20, 2024 By CloudZero In CloudZero
As cloud systems become increasingly sophisticated, you want a cloud monitoring platform that helps you identify, isolate, and fix root-cause issues. Meanwhile, engineering leaders are under increasing pressure to reduce technology costs as the global economic outlook remains uncertain. With Datadog, you can observe, monitor, analyze, and report on the health of your infrastructure, applications, and services in any cloud and at scale.
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datadog

How to support a growing Kubernetes cluster with a small etcd

Dec 20, 2024 By David M. Lentz In Datadog
Etcd plays a critical role in your Kubernetes setup: it stores the ever-changing state of your cluster and its objects, and the API server uses this data to manage cluster resources. As your applications thrive and your Kubernetes clusters see more traffic, etcd handles an increasing amount of data. But etcd’s storage space is limited: the recommended maximum is 8 GiB, and a large and dynamic cluster can easily generate enough data to reach that limit.
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canonical

Building RAG with enterprise open source AI infrastructure

Dec 20, 2024 By Michelle Anne Tabirao In Canonical
One of the most critical gaps in traditional Large Language Models (LLMs) is that they rely on static knowledge already contained within them. Basically, they might be very good at understanding and responding to prompts, but they often fall short in providing current or highly specific information.
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