Operations | Monitoring | ITSM | DevOps | Cloud

If you’ve worked with Kubernetes, you know logs are essential for understanding what’s happening inside your clusters. However, unlike traditional servers, Kubernetes logs present their unique challenges. Pods frequently start and stop, containers restart regularly, and logs stored locally can be lost quickly. Because of this, managing logs in Kubernetes requires a different approach.

You’ve probably run a lot of Docker containers, but do you know what happens behind the scenes? The Docker container lifecycle is the path a container follows from being created to running, stopping, and finally getting removed. Understanding these steps helps you figure out why a container might not start or when to restart it instead of creating a new one.

Security and DevOps teams are drowning in data. Fueled by the explosion of cloud-native architectures, microservices, and accelerated software development cycles driven by AI, telemetry volumes are growing faster than ever. For most organizations, security and observability data is now doubling every 2–3 years. At the same time, most of the tools used to analyze that data—SIEMs, log analytics platforms, and cloud-native observability tools—charge based on ingestion volume.

When your Kubernetes app suddenly has traffic spikes in a distant country, it can be difficult to determine why. Let’s say, for example, we have an e-commerce app that started to receive an unusual surge of visitors from Australia — something we never anticipated. We search for answers in our logs, but without geographic context, we don’t have the full insights we need.

Datadog dashboards provide a unified view of your applications, infrastructure, logs, and other observability data—making it easy to monitor health, investigate issues, and share insights across teams. While native Datadog widgets support a broad range of visualization types, some use cases call for more customized representations, particularly when you’re working with unconventional data formats, external sources, or specific transformations.

Hallucinations occur when a large language model (LLM) confidently generates information that is false or unsupported. These responses can spread misinformation that jeopardizes safety, causes reputational damage, and erodes user trust. Augmented generation techniques, such as retrieval-augmented generation (RAG), aim to reduce hallucinations by providing LLMs with relevant context from verified sources and prompting the LLMs to cite these sources in their responses.

Let’s start with the obvious: we’d like to have Anthropic as a customer. We greatly admire the work they are doing at the intersection of frontier models + safety. We use lots of different AI tooling at incident.io. We’re all-in at AI at incident.io, both to improve the productivity of our internal team and, more importantly, to provide our customers with superpowers in the form of an AI incident responder.

At ManageEngine, we're committed to empowering IT teams with tools that simplify operations and deliver effortless observability for all stakeholders. We're excited to announce the Site24x7 extension for ManageEngine ServiceDesk Plus On Demand now available on the ManageEngine Marketplace. This extension transforms ServiceDesk Plus On Demand from a passive ticketing tool into an active hub for IT infrastructure management.

Global cloud spending will surge past $700 billion this year. Megacaps alone will spend more than $300 billion on AI in 2025, with much more on the way. The innovation potential of the cloud has never been higher, never been more hotly contested, and never come with a higher price tag. In the late 2000s, the cloud reshaped the global economy and enabled life as we know it. Now, in the mid 2020s, AI is poised to do the same.

Today, we are happy to celebrate Elastic and AWS committing to a five-year strategic collaboration agreement (SCA). Our collaboration underscores the efforts of Elastic and AWS to provide you with increased speed and greater flexibility as you adopt generative AI technology.