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Sumo Logic Releases HELM Chart V4 Feature

Sumo Logic announced the availability of its HELM Chart V4 feature to fully unify data collection as part of its continued commitment to OpenTelemetry (OTel).

Organizations can now package, configure and deploy applications and services on Kubernetes clusters with OpenTelemetry as a default to simplify the collection of metrics, events, logs and traces.

Sumo Logic HELM Chart V4 removes dependencies on disparate third-party solutions like Fluentbit, Fluentd and Prometheus to reduce data collection complexity and cost. By fully unifying collection for all logs, metrics and traces, organizations can save the cost of managing multiple agents. Organizations can also optimize their deployment lifecycle while minimizing required updates and potential security risks. OTel collection also provides significant performance with less CPU consumption for additional cost efficiencies.

“Sumo Logic is continuing to deliver on our commitment to OpenTelemetry data collection to customers and the community,” said Tej Redkar, Chief Product Officer for Sumo Logic. “Sumo Logic HELM Chart V4 evolves the collection experience for Kubernetes by using OpenTelemetry as its standard collector, and will help our customers get the insights they need to take action to uncover and resolve performance issues quickly, so DevOps teams can spend less time troubleshooting issues, and do what they do best - deploy code.”

Sumo Logic HELM Chart V4 fully unifies the OpenTelemetry pipeline to provide real-time operations insights for digital business through:

- Unified collection - unified Kubernetes monitoring is now available through a single agent for all signals - logs, metrics, traces and events.

- Auto-instrumentation - correlated telemetry and auto-instrumentation provide a simplified collection process to reduce the chaos of managing disparate third-party collection agents to process monitoring signals.

- Pre-canned configurations - running a single agent for all data types allows for a smaller, more efficient data collection footprint, giving customers quicker application infrastructure setup and a smoother experience to help drive adoption with developers and DevOps teams.

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Sumo Logic Releases HELM Chart V4 Feature

Sumo Logic announced the availability of its HELM Chart V4 feature to fully unify data collection as part of its continued commitment to OpenTelemetry (OTel).

Organizations can now package, configure and deploy applications and services on Kubernetes clusters with OpenTelemetry as a default to simplify the collection of metrics, events, logs and traces.

Sumo Logic HELM Chart V4 removes dependencies on disparate third-party solutions like Fluentbit, Fluentd and Prometheus to reduce data collection complexity and cost. By fully unifying collection for all logs, metrics and traces, organizations can save the cost of managing multiple agents. Organizations can also optimize their deployment lifecycle while minimizing required updates and potential security risks. OTel collection also provides significant performance with less CPU consumption for additional cost efficiencies.

“Sumo Logic is continuing to deliver on our commitment to OpenTelemetry data collection to customers and the community,” said Tej Redkar, Chief Product Officer for Sumo Logic. “Sumo Logic HELM Chart V4 evolves the collection experience for Kubernetes by using OpenTelemetry as its standard collector, and will help our customers get the insights they need to take action to uncover and resolve performance issues quickly, so DevOps teams can spend less time troubleshooting issues, and do what they do best - deploy code.”

Sumo Logic HELM Chart V4 fully unifies the OpenTelemetry pipeline to provide real-time operations insights for digital business through:

- Unified collection - unified Kubernetes monitoring is now available through a single agent for all signals - logs, metrics, traces and events.

- Auto-instrumentation - correlated telemetry and auto-instrumentation provide a simplified collection process to reduce the chaos of managing disparate third-party collection agents to process monitoring signals.

- Pre-canned configurations - running a single agent for all data types allows for a smaller, more efficient data collection footprint, giving customers quicker application infrastructure setup and a smoother experience to help drive adoption with developers and DevOps teams.

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 ...