Skip to main content

Honeycomb Telemetry Pipeline and Honeycomb for Log Analytics Released

Honeycomb announced the launch of two groundbreaking products: Honeycomb Telemetry Pipeline and Honeycomb for Log Analytics.

These updates empower organizations to transform how they understand their software systems, and bridges the gap between traditional monitoring and cutting-edge observability practices. Teams can develop greater effectiveness, proactivity, and resilience in managing complex systems.

Honeycomb's new Telemetry Pipeline and Log Analytics features round out its unified observability platform, empowering engineering teams to manage and analyze log data with speed, efficiency, and confidence, transforming observability from a cost center to a value driver.

"Enterprises face a growing challenge as telemetry data increases exponentially, legacy systems struggle to keep pace, and costs spiral out of control," said Christine Yen, CEO and Co-Founder of Honeycomb. "Honeycomb's expanded platform, with the addition of our Telemetry Pipeline and Log Analytics, provides a centralized solution that tames data chaos and unlocks critical insights from logs. This unified view empowers teams to quickly identify, understand, and resolve issues, freeing up time to focus on the innovation that keeps them competitive."

Honeycomb's suite of new features are designed to make it both technically and economically feasible to harness all telemetry data, enabling customers to ask better questions, explore data more effectively, and gain deeper insights into system behavior. They include:

- Honeycomb Telemetry Pipeline: Leverage various data processing capabilities (collect, enrich, filter, sample, route, and more) to derive more value from your telemetry data than ever before. Start with existing data sources and transition over time to advanced observability practices. Our flexible, OpenTelemetry-powered architecture enables scaling without prohibitive costs or technical barriers.

- Honeycomb for Log Analytics: Use the full power and speed of Honeycomb's analysis engine on log data, thanks to a much more log-native experience—no configuring of indexes necessary.

- New Logs homepage: Surfaces insights instantly and enables users to freely group or filter by any fields and values – even custom ones, at no additional cost – to better understand the state of their systems.

- Explore Data function: Allows teams to conduct further open-ended exploration in a table or log line view, enabling teams to scan and parse through log lines sequentially in a single view and run follow-up queries in a single click.

The Latest

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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Honeycomb Telemetry Pipeline and Honeycomb for Log Analytics Released

Honeycomb announced the launch of two groundbreaking products: Honeycomb Telemetry Pipeline and Honeycomb for Log Analytics.

These updates empower organizations to transform how they understand their software systems, and bridges the gap between traditional monitoring and cutting-edge observability practices. Teams can develop greater effectiveness, proactivity, and resilience in managing complex systems.

Honeycomb's new Telemetry Pipeline and Log Analytics features round out its unified observability platform, empowering engineering teams to manage and analyze log data with speed, efficiency, and confidence, transforming observability from a cost center to a value driver.

"Enterprises face a growing challenge as telemetry data increases exponentially, legacy systems struggle to keep pace, and costs spiral out of control," said Christine Yen, CEO and Co-Founder of Honeycomb. "Honeycomb's expanded platform, with the addition of our Telemetry Pipeline and Log Analytics, provides a centralized solution that tames data chaos and unlocks critical insights from logs. This unified view empowers teams to quickly identify, understand, and resolve issues, freeing up time to focus on the innovation that keeps them competitive."

Honeycomb's suite of new features are designed to make it both technically and economically feasible to harness all telemetry data, enabling customers to ask better questions, explore data more effectively, and gain deeper insights into system behavior. They include:

- Honeycomb Telemetry Pipeline: Leverage various data processing capabilities (collect, enrich, filter, sample, route, and more) to derive more value from your telemetry data than ever before. Start with existing data sources and transition over time to advanced observability practices. Our flexible, OpenTelemetry-powered architecture enables scaling without prohibitive costs or technical barriers.

- Honeycomb for Log Analytics: Use the full power and speed of Honeycomb's analysis engine on log data, thanks to a much more log-native experience—no configuring of indexes necessary.

- New Logs homepage: Surfaces insights instantly and enables users to freely group or filter by any fields and values – even custom ones, at no additional cost – to better understand the state of their systems.

- Explore Data function: Allows teams to conduct further open-ended exploration in a table or log line view, enabling teams to scan and parse through log lines sequentially in a single view and run follow-up queries in a single click.

The Latest

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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...