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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

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...