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Logz.io Unveils Enhancements to Observability Platform

Logz.io announced notable updates to its observability platform. Available later this year, the combination of capabilities including Unified Dashboards, Anomaly Detection, Service Performance Monitoring, and Security Event Management, supported by further adoption of OpenSearch, deliver a wide array of more advanced and mission-critical capabilities needed to support today’s unified full stack observability.

New product enhancements announced at ScaleUP 2021 are:

- Unified Dashboards: Enables customers to quickly search for the root cause of production issues and perform centralized analysis across their full observability stack – using any combination of logs, metrics, and traces monitored using Logz.io. Allows customers to quickly consolidate data without manually switching between products and unify any/all of their data in unified dashboards to get the full picture of their environment, enable teams, and resolve problems sooner.

- Anomaly Detection: Harnesses the power of AI to allow customers to automatically isolate and investigate emerging problems in their environments, based on unusual patterns and behaviors. To locate and alert on issues, Logz.io AI engine monitors data and builds a model that takes into account unique trends in the environment and begins monitoring for unusual behavior on an ongoing basis. The system improves precision over time based on the continued application of machine learning and human inputs.

- Service Performance Monitoring: Advances the ability of customers to monitor application performance in microservices architectures using Logz.io Distributed Tracing, based on Jaeger. Enables users to gain a bird’s eye view into their systems’ health metrics to quickly locate anomalies and spikes and drill down to sample traces with a single click. Based on Logz.io’s work with the OSS community to enhance Jaeger and OpenTelemetry, including contribution of the OTEL Span Metrics Processor.

- Security Event Management: Delivers capabilities required by security analysts to better classify, prioritize and collaborate on the mitigation of events, gaining the specific ability to enact targeted workflows aimed at threat response. Event Management supports key workflows around emerging security threats identification, assignment and handling, severity-based triage and subsequent mitigation – providing teams with visibility into event trends, resolution status and other key response metrics.

- OpenSearch: In keeping with its strong commitment to open source, Logz.io has adopted OpenSearch as its backend engine for logs and search in its cloud native observability platform. Since Elastic announced its intent to close-source Elasticsearch, Logz.io has worked closely with AWS and others to advance OpenSearch, enabling users to safeguard their existing investments while providing faster, more responsive log data ingestion, querying and analysis.

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In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...

Logz.io Unveils Enhancements to Observability Platform

Logz.io announced notable updates to its observability platform. Available later this year, the combination of capabilities including Unified Dashboards, Anomaly Detection, Service Performance Monitoring, and Security Event Management, supported by further adoption of OpenSearch, deliver a wide array of more advanced and mission-critical capabilities needed to support today’s unified full stack observability.

New product enhancements announced at ScaleUP 2021 are:

- Unified Dashboards: Enables customers to quickly search for the root cause of production issues and perform centralized analysis across their full observability stack – using any combination of logs, metrics, and traces monitored using Logz.io. Allows customers to quickly consolidate data without manually switching between products and unify any/all of their data in unified dashboards to get the full picture of their environment, enable teams, and resolve problems sooner.

- Anomaly Detection: Harnesses the power of AI to allow customers to automatically isolate and investigate emerging problems in their environments, based on unusual patterns and behaviors. To locate and alert on issues, Logz.io AI engine monitors data and builds a model that takes into account unique trends in the environment and begins monitoring for unusual behavior on an ongoing basis. The system improves precision over time based on the continued application of machine learning and human inputs.

- Service Performance Monitoring: Advances the ability of customers to monitor application performance in microservices architectures using Logz.io Distributed Tracing, based on Jaeger. Enables users to gain a bird’s eye view into their systems’ health metrics to quickly locate anomalies and spikes and drill down to sample traces with a single click. Based on Logz.io’s work with the OSS community to enhance Jaeger and OpenTelemetry, including contribution of the OTEL Span Metrics Processor.

- Security Event Management: Delivers capabilities required by security analysts to better classify, prioritize and collaborate on the mitigation of events, gaining the specific ability to enact targeted workflows aimed at threat response. Event Management supports key workflows around emerging security threats identification, assignment and handling, severity-based triage and subsequent mitigation – providing teams with visibility into event trends, resolution status and other key response metrics.

- OpenSearch: In keeping with its strong commitment to open source, Logz.io has adopted OpenSearch as its backend engine for logs and search in its cloud native observability platform. Since Elastic announced its intent to close-source Elasticsearch, Logz.io has worked closely with AWS and others to advance OpenSearch, enabling users to safeguard their existing investments while providing faster, more responsive log data ingestion, querying and analysis.

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Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...