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New Relic Announces New Log Management Capabilities

New Relic added new capabilities to its log management feature:

- The ability to detect patterns and outliers in log data

Now out of beta and broadly available, you can reduce troubleshooting time by using machine learning to detect clustered patterns to surface outliers in log data automatically. This capability enables you to quickly find patterns to reduce noise and create queries, alerts, and dashboards based on those patterns for deeper analysis.

- Simple, more intuitive analytics

New Relic made significant changes in the Logs UI. For example, you can efficiently find and use advanced features directly from the main screen. You’ll find more UI space dedicated to logs, making it easier to see the details and debug faster. This updated UI experience also includes colorization of log levels, a message summary, expanded dashboard visualization for displaying long log messages directly in dashboards, and more.

- Partitioning of data any way you want for fast search performance

Massively scaled search performance based on data partitioning provides flexibility to segment data in a way that makes sense for your teams, use cases, or logical groupings. You can optimize searches against high volumes of data to search one or multiple partitions and quickly return the results needed.

- Easier log onboarding

New Relic made it even easier to get log data into New Relic One. The recently released guided install enables you to find additional logs in common locations that will help expand your visibility while also making it easier to onboard higher volumes of data. New Relic also added Native Heroku cloud support to expand out-of-box support for one of the most common cloud sources used by developers. And they added an option to bring in data without needing to install or maintain any third-party software, with agentless syslog support through our TCP endpoint.

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New Relic Announces New Log Management Capabilities

New Relic added new capabilities to its log management feature:

- The ability to detect patterns and outliers in log data

Now out of beta and broadly available, you can reduce troubleshooting time by using machine learning to detect clustered patterns to surface outliers in log data automatically. This capability enables you to quickly find patterns to reduce noise and create queries, alerts, and dashboards based on those patterns for deeper analysis.

- Simple, more intuitive analytics

New Relic made significant changes in the Logs UI. For example, you can efficiently find and use advanced features directly from the main screen. You’ll find more UI space dedicated to logs, making it easier to see the details and debug faster. This updated UI experience also includes colorization of log levels, a message summary, expanded dashboard visualization for displaying long log messages directly in dashboards, and more.

- Partitioning of data any way you want for fast search performance

Massively scaled search performance based on data partitioning provides flexibility to segment data in a way that makes sense for your teams, use cases, or logical groupings. You can optimize searches against high volumes of data to search one or multiple partitions and quickly return the results needed.

- Easier log onboarding

New Relic made it even easier to get log data into New Relic One. The recently released guided install enables you to find additional logs in common locations that will help expand your visibility while also making it easier to onboard higher volumes of data. New Relic also added Native Heroku cloud support to expand out-of-box support for one of the most common cloud sources used by developers. And they added an option to bring in data without needing to install or maintain any third-party software, with agentless syslog support through our TCP endpoint.

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For many B2B and B2C enterprise brands, technology isn't a core strength. Relying on overly complex architectures (like those that follow a pure MACH doctrine) has been flagged by industry leaders as a source of operational slowdown, creating bottlenecks that limit agility in volatile market conditions ...

FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...

Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...

While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...

A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...