When we created Cribl Search, we wanted to give systems administrators the ability to query data without having to spend resources on collection and processing first — but we didn’t stop there. With Search, we’re also making it possible to query all the data you’ve already collected, processed, and kept in places like object stores, file systems, analytics tools, S3 buckets, or other data stores.

Our product strategy this year was relatively simple. Many observability practitioners we spoke with complained that observability was oftentimes slow, heavy, complex, and costly – which can be summed up in our CEO’s recent blog on modern observability challenges. While our customers didn’t report similar challenges, we wanted to further distance ourselves from this typical observability experience.

Whether you are a site reliability engineer, DevOps engineer, or application developer, you need visibility into the health and performance of every service you run or support. But in complex, dynamic environments, it can be difficult to ensure that all services are accounted for.

Today, I’m thrilled to announce the introduction of Logz.io Open 360™. This is a major step in our journey. Open 360 is a unified platform for modern engineering teams requiring end-to-end observability across logs, metrics and traces—delivered in an intuitive user interface. Open 360™ is specifically designed to enable engineers to have deep monitoring and insights into distributed systems.

Many organizations face complex regulatory requirements when it comes to monitoring the health and performance of their service and application infrastructure. As part of our ongoing commitment to providing a comprehensive monitoring solution for all customers, we’re pleased to announce that Datadog has achieved TISAX Assessment Level 2 (AL2) certification.

Today, we’re announcing the expansion of Honeycomb integrations with various AWS services. This update now covers a much wider swath of AWS services, makes it easier to integrate your AWS stack with Honeycomb, and with our new BubbleUp enhancements, you’ll be identifying and debugging hidden issues in your AWS stack faster than ever.

There is significant momentum around observability, as detailed in VMware’s 2022 State of Observability report, with almost all respondents stating that observability would benefit their organization. This is further validated by Gartner including observability in their Magic Quadrant for Application Performance Monitoring and Observability report for the first time this year.

What’s the ultimate goal of bringing observability into an organization? Is it just to chase down things when they’re broken and not working? Or can it be used to truly enable developers to innovate faster? That’s a topic I recently discussed with David Ostrovsky, a software engineer at Meta, the parent company of social media networks Facebook and Instagram among others. He was my guest on the most recent episode of the OpenObservability Talks podcast.

Distributed architectures are becoming an increasingly important source of application services for organizations. Advances in observability and monitoring are being driven by this trend. But exactly how do observability and monitoring differ from one another? It's essential to know when something goes wrong in the application delivery chain so you can identify the root cause and resolve it before it has an impact on your business. Monitoring and observability offer a two-pronged strategy.

The transition to distributed applications is in full swing, driven mainly by our need to be “always-on” as consumers and fast-paced businesses. That need is driving deployments to have more complex requirements along with the ability to be globally diverse and rapidly innovate.

Mission-critical apps that are deployed on the cloud drive today's modern enterprises, which in turn power their businesses. These applications' fundamental units are microservices, which tiny development teams created to enable speedy feature releases to the market. APIs serve as the ties that bring these microservices together so they can cooperate.

Today's complex environments require IT teams to find new ways to monitor and measure SAP — but traditional SAP monitoring tools can't cut it. Here's how to rethink your approach to SAP monitoring.

The gaming industry is an extensive software market segment, reaching over $225 billion US in 2022. This staggering number represents gaming software sales to users with high expectations of game releases. User acquisition takes up a large part of software budgets, with $14.5 billion US spending globally in 2021. User retention is critical to the success of any game, especially where monetization requires driving in-app purchases and ad revenue.

California-based Medallia captures feedback signals — in-person interactions, customer surveys, call centers, social media, etc. — to help businesses improve their customer experience. In much the same way, the company’s Performance and Observability Engineering team captures observability signals to optimize the experience for internal users.

The traditional approach for searching observability data is a tried-and-true: Once all the search staging is accomplished, we can perform high-speed, high-performance, deep-dive analysis of the data. But is this the best way or even the only way to search all that observability data? The answer to the first question is maybe, as it depends on what you are trying to accomplish. The answer to the second question must be a resounding no.

Over the course of two decades, Adform grew from a dream between friends huddled in a basement to a leading advertising tech platform powering more than 25,000 clients worldwide. Success brought external accolades, but it also created the need for internal innovation to support the company’s continued growth. In 2018, Adform was still operating in startup mode, which meant developers and teams cherry-picked the tools that worked best for them.

With the introduction of Environments & Services, we’ve seen a dramatic increase in the creation of new datasets. These new datasets are smaller than ones created with Honeycomb Classic, where customers would typically place all of their services under a single, large dataset. This change has presented some interesting scaling challenges, which I’ll detail in this post, along with the solution we used, and how we leveraged Honeycomb’s own telemetry to scale Honeycomb.

Intercom’s mission is to build better communication between businesses and their customers. With that in mind, they began their journey away from metrics alone and towards complete observability. The first step was tooling, and they learned quickly that trying to work with multiple solutions was not the answer.

Our recent blog post about observability pipelines highlighted how they centralize and enable data actionability. A key benefit of observability pipelines is users don't have to compare data sets manually or rely on batch processing to derive insights, which can be done directly while the data is in motion. As a result, teams get access to the data they need to make decisions faster.

If you’re writing software today, then you likely use a CI/CD pipeline to build and test your code before deploying it to production. Having a fast and efficient build pipeline saves you development time, shortens feedback loops, and helps you ship features faster. Conversely, slow and unreliable build pipelines are full of lost productivity and sadness.

An observability pipeline is a tool or process that centralizes data ingestion, transformation, correlation, and routing across a business. Production engineers across ITOps, Development, and Security teams use them to more efficiently and cost-effectively transform their telemetry data to drive critical decisions. Businesses of all sizes can enjoy several benefits and gain a significant competitive advantage by implementing an observability pipeline.

If you’re an engineer trying to fix real problems with your apps, looking at just one small part of the picture isn’t going to cut it. This is why Kentik is so focused on helping you understand what’s going on beyond single k8s instances, and it’s a big part of what network observability is all about. This was Kentik’s message at Kubecon 2022, which was a memorable event for us.

The Lambda Telemetry API empowers users to integrate monitoring and observability tools like Site24x7 with their Lambda functions. Site24x7 is an AWS-reviewed Lambda Service Ready Program Partner and is announced as a launch partner in AWS Lambda Telemetry API feature release. Customers, AWS partners, and the serverless community can use the Lambda Telemetry API to receive telemetry streams from the Lambda service, including function, extension logs, and metrics coming from the Lambda platform.

There are a ton of leaves in my yard, and I’m slowly coming out of a weeklong sugar coma. That can only mean October has come and gone. Let’s take a peek at what new and noteworthy changes Honeycomb has made since we last checked in.

Here at Grafana Labs, we’re deeply committed to our “big tent” philosophy — the idea that disparate data sources, from different software providers, in different industries, built for completely different use cases, can come together in one composable observability platform. As part of that commitment, we’ve set out to hear from our community about their observability practice and what they hope to see in this space in the future.

Cribl, a leader in open observability, today released Cribl Search, the first federated query engine focused on observability and security data. Search flips the observability market on its head, dispatching queries to where the data is already at rest. Cribl Search was engineered to let you search data-in-place, whether the data remains at the edge, in the stream, in the observability lake, at the endpoint, or even in existing search tools.

One of the things that struck me upon joining Honeycomb was the seemingly laissez-faire approach we took towards internal SLOs. From my own research (beginning with the classic SRE book, following Google’s example), I came to these conclusions: If you read the original SRE book when it was released, before the workbook came out, these conclusions all made sense.

An avid bird-watcher once told me that for bird-watching beginners, it’s more important to focus on learning about the birds and identifying their unique songs rather than trying to find the perfect pair of binoculars.

Throughout KubeCon + CloudNativeCon NA 2022, our team was able to speak to over 100 people from the cloud-native community to learn more about their thoughts and experience of the event. This blog will explore what the community thought was the hot topic of discussion at KubeCon + CNC NA 2022, which includes topics such as security, cost, and developer experience. Check out the full video below.

Many people wonder what the difference is between monitoring vs. observability. While monitoring is simply watching a system, observability means truly understanding a system’s state. DevOps teams leverage observability to debug their applications or troubleshoot the root cause of system issues. Peak visibility is achieved by analyzing the three pillars of observability: Logs, metrics and traces.

In an era where there’s no shortage of established best practices and tools, engineering teams are consistently finding their ability to prevent, detect and resolve production issues is only getting harder. Why is this the case? Our most recent DevOps Pulse Survey highlighted alarming trends to this end.

At Kentik, we pride ourselves as innovators and thought-leaders for network observability. “Kentik is network observability” is more than a slogan for us. It’s an idea that informs our product roadmap and guides our problem-solving with customers. We’ve done a lot to explain network observability to prospects.

A few weeks back, I got the chance to sit down with our very own Jordan Perks from the Cribl Customer Success Team. Jordan is an Observability subject matter expert AND knows a thing or two about Cribl Products! After geeking out a bit about data best practices, we started chatting about enabling our customer champions to have different conversations with stakeholders across their organizations. When someone becomes an observability engineer, they step into a much different role.

In today's complex, multi-cloud environments, IT and engineering teams are under increasing pressure to respond to errors affecting their entire system. Therefore, IT operations, DevOps, and SRE teams are all striving to gain complete observability across these increasingly complex and diverse computing environments. But what exactly does observability mean?

The way businesses obtain infrastructure has changed dramatically over the past decade, as Infrastructure-as-a-Service (IaaS) has taken the place of self-hosted infrastructure for most IT deployments. At the same time, it has become common to build complex infrastructures that blend components from multiple providers – such as two or more public clouds (aka. multicloud infrastructure) or mixing an on-prem data center and a public cloud (aka. hybrid cloud infrastructure).

In the last few years, the usage of databases that charge by request, query, or insert—rather than by provisioned compute infrastructure (e.g., CPU, RAM, etc.)—has grown significantly. They’re popular for a lot of the same reasons that serverless compute functions are, as the cost will scale with your usage. No one is using your site? No problem: you’re not charged.

Today, during the ObservabilityCon 2022 keynote session, we announced a new open source project for frontend application observability, Grafana Faro. The project is launching with a highly configurable web SDK that instruments web applications to capture observability signals. This frontend telemetry can then be correlated with backend and infrastructure data for seamless, full-stack observability. There’s supposed to be a video here, but for some reason there isn’t.

Grafana Labs CEO and Co-founder Raj Dutt sat down with “NYSE Floor Talk” ahead of ObservabilityCON to discuss why companies are increasingly focused on observability as a means to improve customer satisfaction. In his conversation with Judy Khan Shaw, host of “NYSE Floor Talk,” Dutt also talked about Grafana Labs’ big tent philosophy and the growth of Grafana Labs and the Grafana open source community.

As an operations engineer (SRE, IT manager, DevOps), you’re always struggling with how to manage technology and data sprawl. Kubernetes is becoming increasingly pervasive and a majority of these deployments will be in Amazon Elastic Kubernetes Service (EKS), Google Kubernetes Engine (GKE), or Azure Kubernetes Service (AKS). Some of you may be on a single cloud while others will have the added burden of managing clusters on multiple Kubernetes cloud services.