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New Loggly Derived Fields Automatically Inject Intelligence and Structure into Any Log

Loggly announced Loggly Derived Fields, a new capability within Loggly Pro and Enterprise that allows users to specify custom parsing rules that generate derived fields as metadata during the ingestion process.

The derived fields provide intelligence and structure that is then used by Loggly Dynamic Field Explorer; to automatically catalog and summarize logs for one-click navigation and analysis. With derived fields and Field Explorer, development and DevOps teams can troubleshoot problems faster, more effectively monitor their log data for emerging issues, and do sanity checks after code releases without relying on cumbersome, manual queries.

A significant proportion of logs in organizations today are unstructured; the data is not separated into discrete data elements that allow for focused searching or aggregate metrics. Unstructured logs create considerable manual effort for DevOps professionals who traditionally have had to create one-off regular expressions and custom analyses. Many log management solutions require users to develop queries from scratch for each analysis, which is time consuming and inhibits sharing with less technical team members. Derived fields are different because once users create their rules, Loggly automatically applies those rules to all log data it receives, for all users of the account. Any user is then able to use Field Explorer to navigate by field names and values and to create sophisticated analyses without doing any additional work.

Derived fields expand the functionality of the flagship interface of Loggly, Loggly Dynamic Field Explorer. While most traditional log management solutions are designed with the search box as the primary interface, Field Explorer generates navigable lists and summaries to deliver a guided search experience. With Field Explorer, users get a bird’s-eye view of what’s happening, can quickly spot anomalies, understand the magnitude of issues, and search their logs with greater precision. Loggly Derived Fields extend the capabilities of Field Explorer to virtually any log type, structured or unstructured.

Derived fields are metadata, allowing for the original log data to stay intact so that logs can be analyzed in multiple ways. A user may create as many derived fields as needed from the same log events. Derived fields and Dynamic Field Explorer together help DevOps and IT professionals identify and resolve systems issues faster through more flexible and efficient log analytics.

“Unstructured logs are a reality for most cloud-centric companies, yet being able to efficiently mine those logs is critical for troubleshooting and delivering high application responsiveness to the business and end customers,” says Hector Angulo, Head of Product at Loggly. “Derived fields are another step in our unique ‘summarize first’ strategy. Customers can now gain insight even faster and spend more of their time solving or preventing IT issues rather than finding them.”

Loggly Derived Fields are currently in beta and will be generally available by the end of June 2015. Derived fields are a feature of the Loggly Pro and Enterprise subscription plans.

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New Loggly Derived Fields Automatically Inject Intelligence and Structure into Any Log

Loggly announced Loggly Derived Fields, a new capability within Loggly Pro and Enterprise that allows users to specify custom parsing rules that generate derived fields as metadata during the ingestion process.

The derived fields provide intelligence and structure that is then used by Loggly Dynamic Field Explorer; to automatically catalog and summarize logs for one-click navigation and analysis. With derived fields and Field Explorer, development and DevOps teams can troubleshoot problems faster, more effectively monitor their log data for emerging issues, and do sanity checks after code releases without relying on cumbersome, manual queries.

A significant proportion of logs in organizations today are unstructured; the data is not separated into discrete data elements that allow for focused searching or aggregate metrics. Unstructured logs create considerable manual effort for DevOps professionals who traditionally have had to create one-off regular expressions and custom analyses. Many log management solutions require users to develop queries from scratch for each analysis, which is time consuming and inhibits sharing with less technical team members. Derived fields are different because once users create their rules, Loggly automatically applies those rules to all log data it receives, for all users of the account. Any user is then able to use Field Explorer to navigate by field names and values and to create sophisticated analyses without doing any additional work.

Derived fields expand the functionality of the flagship interface of Loggly, Loggly Dynamic Field Explorer. While most traditional log management solutions are designed with the search box as the primary interface, Field Explorer generates navigable lists and summaries to deliver a guided search experience. With Field Explorer, users get a bird’s-eye view of what’s happening, can quickly spot anomalies, understand the magnitude of issues, and search their logs with greater precision. Loggly Derived Fields extend the capabilities of Field Explorer to virtually any log type, structured or unstructured.

Derived fields are metadata, allowing for the original log data to stay intact so that logs can be analyzed in multiple ways. A user may create as many derived fields as needed from the same log events. Derived fields and Dynamic Field Explorer together help DevOps and IT professionals identify and resolve systems issues faster through more flexible and efficient log analytics.

“Unstructured logs are a reality for most cloud-centric companies, yet being able to efficiently mine those logs is critical for troubleshooting and delivering high application responsiveness to the business and end customers,” says Hector Angulo, Head of Product at Loggly. “Derived fields are another step in our unique ‘summarize first’ strategy. Customers can now gain insight even faster and spend more of their time solving or preventing IT issues rather than finding them.”

Loggly Derived Fields are currently in beta and will be generally available by the end of June 2015. Derived fields are a feature of the Loggly Pro and Enterprise subscription plans.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.