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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.

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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

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...