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Sumo Logic Introduces Unified Logs and Metrics for Applications Running on Kubernetes and Docker

Sumo Logic announced a new unified logs and metrics solution for improving customer experience of applications running on Kubernetes and Docker.

Sumo Logic streamlines the data ingestion process using open-source and native integrations widely adopted for Kubernetes and Docker, and provides improved visualization and optimized analytics to show the health of Kubernetes-powered applications. This unified approach gives users full visibility and continuous intelligence into their application and microservices architecture, and helps them quickly identify and troubleshoot issues and improve customer experience.

Today’s modern applications are more like living organisms than the static, monolithic architectures of the past. Microservice architectures and container technologies such as Kubernetes and Docker are part of these new modern applications and are growing in popularity. According to exclusive customer usage data from Sumo Logic, microservices and container adoption is increasing, with 25 percent of cloud customers adopting Docker and around 35 percent of Docker users adopting orchestration solutions like Kubernetes. While microservices and container technologies are helping enterprises adopt multi-cloud services as they seamlessly abstract application from the underlying infrastructures, as new functionality is released, monitoring and troubleshooting of container-based services must adapt as well.

Sumo Logic’s multi-tenant data analytics platform and machine learning capabilities enable organizations to realize their full data insight potential to build, run, secure and manage modern applications, regardless of the underlying infrastructure and technology stack. By expanding native integrations with microservice-based modern applications and container technology, the broader support for Kubernetes provides users with a comprehensive, in-depth, and real-time picture of applications running on Kubernetes – such as services, namespaces, nodes, pods, and containers.

In addition, Sumo Logic provides out-of-the-box data exploration and visualization capabilities to understand the real-time state of their application within Kubernetes.

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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Sumo Logic Introduces Unified Logs and Metrics for Applications Running on Kubernetes and Docker

Sumo Logic announced a new unified logs and metrics solution for improving customer experience of applications running on Kubernetes and Docker.

Sumo Logic streamlines the data ingestion process using open-source and native integrations widely adopted for Kubernetes and Docker, and provides improved visualization and optimized analytics to show the health of Kubernetes-powered applications. This unified approach gives users full visibility and continuous intelligence into their application and microservices architecture, and helps them quickly identify and troubleshoot issues and improve customer experience.

Today’s modern applications are more like living organisms than the static, monolithic architectures of the past. Microservice architectures and container technologies such as Kubernetes and Docker are part of these new modern applications and are growing in popularity. According to exclusive customer usage data from Sumo Logic, microservices and container adoption is increasing, with 25 percent of cloud customers adopting Docker and around 35 percent of Docker users adopting orchestration solutions like Kubernetes. While microservices and container technologies are helping enterprises adopt multi-cloud services as they seamlessly abstract application from the underlying infrastructures, as new functionality is released, monitoring and troubleshooting of container-based services must adapt as well.

Sumo Logic’s multi-tenant data analytics platform and machine learning capabilities enable organizations to realize their full data insight potential to build, run, secure and manage modern applications, regardless of the underlying infrastructure and technology stack. By expanding native integrations with microservice-based modern applications and container technology, the broader support for Kubernetes provides users with a comprehensive, in-depth, and real-time picture of applications running on Kubernetes – such as services, namespaces, nodes, pods, and containers.

In addition, Sumo Logic provides out-of-the-box data exploration and visualization capabilities to understand the real-time state of their application within Kubernetes.

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...