DX Unified Infrastructure Management (DX UIM) is a powerful solution that enables IT operations teams to monitor and manage the performance and availability of their IT infrastructure and applications. One of the key core components of DX UIM is the Discovery Server probe. This probe collects, processes, and stores information about devices and applications. In this blog, we will explore some of the benefits and use cases for Discovery Server.

Your applications are only as powerful as they are iterable. To keep up with their rapidly changing production environments, your teams need reliable CI/CD systems that implement best practices—including build and test automation, flaky test management, and deployment management. By optimizing their CI/CD pipelines, your teams can build their apps more efficiently, deploy them more safely, and catch bugs and security vulnerabilities before they make it to production.

In a keynote at AI.Dev, Robert Nishihara (CEO, Anyscale) described the shift: A year ago, the people working with ML models were ML experts. Now, they’re developers. A year ago, the process was to experiment with building a model, then put a product on top of it. Now, it’s ship a product, find the market fit, then create customized models. The general-purpose generative AI models available to all of us today (such as ChatGPT) change the way work is done.

Tucker Callaway (CEO, Mezmo) and Kevin Petrie (Vice President of Research, Eckerson Group) had a conversation centered around enterprises taking control of their data and the growing need for consolidated collection and management of telemetry data. They discuss how enterprises can optimize telemetry pipelines, take charge of their data, and enhance their observability and security game.

This is the second post in a 3-part series about shifting Observability left. If you have not had a chance to read the first, you can find it here. In today’s complex microservices deployments, gaining visibility into deployments is vital for optimal system performance and scalability. This has become even more important as the tech industry has moved toward microservice architecture reliance. Navigating through logs has become increasingly complex as requirements have grown.

IT monitoring is evolving rapidly, and LogicMonitor is at the forefront of this transformation with the release of LogicMonitor’s new user interface (UI). This release marks a significant milestone, reflecting our commitment to innovation, responsiveness to user feedback, and anticipation of future technological trends.

Observability is often defined in the context of three pillars: logs, metrics, and traces. Modern-day cloud-native applications are complex and dynamic. To avoid surprises and performance issues, you need a robust observability stack. But is observability limited to collecting logs, metrics, and traces? How is observability evolving to make our systems more observable? In this tutorial, we cover.

For businesses reliant on customers’ positive digital experiences to achieve their goals, the seamless operation of cloud applications and infrastructure is paramount for financial success. Observability holds a pivotal role in modern enterprises, offering critical insights into your IT system’s health and performance. However, persistent issues of complexity and high costs have plagued the observability landscape.

This is the first post in a 3-part series about shifting Observability left. When it comes to the reliability and performance of your applications, compromise is not an option in the world of software development. This is where observability can help developers achieve a more robust and scalable infrastructure.

The backbone of a modern organization—its IT infrastructure—is intricately woven. Along with the relentless pursuit of achieving seamless operations and sustained growth, the challenges of a modern IT infrastructure led to the proliferation of specialized sub-teams. These specialized teams collaborate to contribute to the health and performance of the IT infrastructure.

Cloud-based database providers often provide great observability out of the box. But, what if you’re developing a tricky feature locally and need more details about what your local Clickhouse is doing? There are many options, but if you’re a numbers and graphs person like me, you’ll want to be able to view the inner workings of Clickhouse in something like Grafana.

Today marks an exciting milestone at Honeycomb, and we're thrilled to share it with you. We officially launched our integration with Microsoft Teams, a step forward in our continuous effort to streamline and enhance your observability experience. Teams now joins our growing list of over 100 Honeycomb integrations.

Observe is a SaaS based observability tool built on Snowflake. It offers a graph-style approach to observability data, claiming that this makes it easier to correlate data in a seamless fashion. Let’s see how Observe compares to Coralogix.

Large Language Models (LLMs) are advanced artificial intelligence models designed to comprehend and generate human-like language. With millions or even billions of [parameters, these models, like GPT-3, excel in natural language processing, understanding context, and generating coherent and contextually relevant text across various applications.

I began working with database customers back in the day with VividCortex until it was purchased by SolarWinds. Since then, I’ve had the opportunity to work with tons of our database solution customers as an account manager and now lead our DPM renewals initiative. In these roles, I’ve helped our customers transition from VividCortex to Database Performance Monitor (DPM) and now migrate into Database Observability.

Kentik now provides network insight into Oracle Cloud Infrastructure (OCI) workloads, allowing customers to map, query, and visualize OCI, hybrid, and multi-cloud traffic and performance.

Why shared intelligence across business, security and application performance is a pivotal growth driver — and how to achieve it. In a global application survey, 62% of consumers agreed that mobile app security protection and features are equally important. Additional research suggests brands have one shot to get it right — or risk losing 32% of their users after just one poor experience.

In today's digital-first landscape, maintaining the health and performance of your network is critical for the seamless operation of your business and its services. To that end, network observability has emerged as a key concept and discipline in ensuring the robustness and performance of networks. But what is network observability?

Engineering and ITOps teams have an important mission: keeping their software and digital systems performing and reliable. But as we’re about to embrace a new year full of changes, industry shifts, and AI developments, this mission is challenged by increasingly complex environments, technology alternatives, and an overwhelming number of tools available. The result? Overages, tool sprawl, and toil, which all lead to longer times to detect and resolve issues.

As applications in the cloud become more distributed and complex, the Mean Time To Resolution (MTTR) for production issues is getting longer. Modern systems are built with hundreds of distinct, ephemeral, and interconnected cloud components, which can make it exceptionally hard for engineers to understand the current state of their applications, what problems are impacting customers, and why those problems are occurring.

In the dynamic landscape of digital business, the pursuit of delivering exceptional user experiences in every digital interaction continues to be a challenge. Cisco, a pioneer in full-stack observability, announced on November 28 at AWS re:Invent the release of business metrics for Cisco Cloud Observability. Let’s delve into the revolutionary landscape that this innovation is carving for both business owners and technical users.

Most SREs and IT Ops manage Java applications without source code access or communication with AppDev teams. When applications have performance issues those SREs or IT Ops teams deploying and maintaining the infrastructure often have to prove that it is the application at fault and supply information to the app supplier which provides evidence of the issue.

Cloud-native developers and practitioners gathered from around the world to learn, collaborate, and network at KubeCon/CloudNativeCon North America 2023 between November 6th and 9th at McCormick Place in Chicago, IL—myself included. This wasn’t my first time attending—I’ve been coming to KubeCon since 2016—but it was easily one of the most exciting experiences I’ve had as part of the Cloud Native community.

This is the final article of a three-part series. To start at the beginning, read Part 1: Benefiting from multi-cluster setups requires familiarity with common variations and Part 2: Exploring the facets of a multi-cluster observability strategy. As companies scale software production, they lean on Kubernetes as a crucial container orchestration platform for managing, deploying and ensuring software availability.

Imagine this scenario: your platform appears to have an issue. Maybe it has gone down or maybe it has affected a large volume of users or perhaps just a few of those important ones; either way there is a significant problem with it. Users are complaining and are happy to shout about the platform not working on X (formally Twitter).

ShipHero needed a robust, cost efficient observability platform to support DevOps, customer support, and more. Committed to timely service, ShipHero recognizes that the seamless performance of its software is paramount to customer satisfaction. To maintain this high standard, the development team needs the right data at their fingertips to quickly find and solve problems as they occur.

Now that we’ve had time to decompress from KubeCon, we wanted to do a writeup about our collective experience. Six of us spoke at the conference and Charity participated in a panel, so we included short talk recaps.

CloudFabrix, the Robotic Data Automation Fabric inventor, announced “Data Fabric for Observability” with dynamic Data Ingestion and Automation service (DIA) for the Cisco Observability Platform. You can see the powerful combination in play at Cisco Live Melbourne between December 5-8th, 2023. Cisco epitomizes “Experience as the new Digital Currency” with its observability platform.

So you’ve taken a look at the core web vitals for your site and… it’s not looking good. You’re overwhelmed, and you don’t know what change to make because everything seems like too big of a project to make a real difference. There are so many measurements to keep track of and the standards cited seem even scarier. This is extremely normal. Web performance standards can feel impossible to meet for a lot of us.

Grafana Cloud constantly evolves to include new, cutting-edge features for end-to-end observability. In fact, just last month at ObservabilityCON 2023, we made a number of updates to our fully managed observability platform, including the general availability of Grafana Cloud Application Observability, Grafana SLO, and Adaptive Metrics.