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Mezmo Unveils Observability Pipeline

Mezmo unveiled its Observability Pipeline, which enables teams to control, enrich, and correlate machine data for actionable insights and faster decisions.

Mezmo's Observability Pipeline helps organizations better control their observability data and deliver increasing business value. It centralizes the flow of data from various sources, adds context to make data more valuable, and then routes it to destinations to drive actionability.

“Data provides a competitive advantage, but organizations struggle to extract real value. First-generation observability data pipelines focus primarily on data movement and control, reducing the amount of data collected, but fall short on delivering value. Preprocessing data is a great first step,” said Tucker Callaway, CEO, Mezmo. “We’ve built on that foundation and our success in making log data actionable to create a smart observability data pipeline that enriches and correlates high volumes of data in motion to provide additional context and drive action.”

Mezmo’s Observability Pipeline provides access and control to ensure that the right data is flowing into the right systems in the right format for analysis, minimizing costs and enabling new workflows. This smart pipeline integrates Mezmo’s best-in-class log analysis features, including search, alerting, and visualization capabilities, to augment and analyze data in motion, delivering intelligent, actionable insights to mitigate risk and make decisions faster.

The flexible, easy-to-use solution enriches workflows, streamlines the adoption of best practices, and enables new observability data use cases. Customers can route data from any source, such as cloud platforms, Fluentd, Logstash, Syslog, and others, to many destinations for various use cases, including Splunk, S3, and Mezmo’s Log Analysis platform.

Support for OpenTelemetry further helps simplify the ingestion of data and makes data more actionable with enrichment of the OpenTelemetry attributes.

Mezmo also helps transform sensitive data to meet regulatory and compliance requirements, such as PII. Control features simplify the management of multiple sources and destinations while protecting against runaway data flow.

The Latest

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 ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Mezmo Unveils Observability Pipeline

Mezmo unveiled its Observability Pipeline, which enables teams to control, enrich, and correlate machine data for actionable insights and faster decisions.

Mezmo's Observability Pipeline helps organizations better control their observability data and deliver increasing business value. It centralizes the flow of data from various sources, adds context to make data more valuable, and then routes it to destinations to drive actionability.

“Data provides a competitive advantage, but organizations struggle to extract real value. First-generation observability data pipelines focus primarily on data movement and control, reducing the amount of data collected, but fall short on delivering value. Preprocessing data is a great first step,” said Tucker Callaway, CEO, Mezmo. “We’ve built on that foundation and our success in making log data actionable to create a smart observability data pipeline that enriches and correlates high volumes of data in motion to provide additional context and drive action.”

Mezmo’s Observability Pipeline provides access and control to ensure that the right data is flowing into the right systems in the right format for analysis, minimizing costs and enabling new workflows. This smart pipeline integrates Mezmo’s best-in-class log analysis features, including search, alerting, and visualization capabilities, to augment and analyze data in motion, delivering intelligent, actionable insights to mitigate risk and make decisions faster.

The flexible, easy-to-use solution enriches workflows, streamlines the adoption of best practices, and enables new observability data use cases. Customers can route data from any source, such as cloud platforms, Fluentd, Logstash, Syslog, and others, to many destinations for various use cases, including Splunk, S3, and Mezmo’s Log Analysis platform.

Support for OpenTelemetry further helps simplify the ingestion of data and makes data more actionable with enrichment of the OpenTelemetry attributes.

Mezmo also helps transform sensitive data to meet regulatory and compliance requirements, such as PII. Control features simplify the management of multiple sources and destinations while protecting against runaway data flow.

The Latest

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 ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...