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Elastic 8.1 Released

Elastic released Elastic 8.1.

New enhancements enable customers to stop advanced cyber threats with new prebuilt detections and data source integrations, and accelerate application development with deeper visibility into serverless architectures and continuous integration and continuous delivery (CI/CD) pipelines.

With enhanced end-to-end application performance monitoring visibility, customers can now collect traces from AWS Lambda, in beta, and correlate those traces with other Elastic Observability data—including from CI/CD environments—for faster and more comprehensive root cause analysis.

Additionally, support for OpenTelemetry logs, also in beta, enables organizations that use OpenTelemetry for traces and metrics to standardize data collection across all data types. The ability to ingest OpenTelemetry logs provides customers an opportunity to deploy a standardized, vendor-neutral observability architecture without losing correlation between signal types and layers.

Now generally available, the ability to enable doc-value-only fields gives customers the flexibility to index data faster while improving storage efficiency. With this new capability, customers can benefit from up to 20% faster indexing speeds and 20% lower data storage requirements, ultimately helping them accelerate time to insights while balancing cost and performance.

Customers can also leverage several new ad hoc analytics capabilities in Kibana Lens to enhance data exploration, including three new visualization types—gauge, waffle, and mosaic—and a new drag-and-drop capability to combine and compare multiple fields.

“As data volumes continue to grow and become more dispersed, cyber threats continue to rise,” said Santosh Krishnan, GM of Elastic Security, Elastic. “... Elastic offers faster indexing speeds, new prebuilt detections, and even more data source integrations to help analysts automate detection, improve prioritization, and accelerate threat analysis. These enhanced capabilities extend user visibility across digital ecosystems—including serverless architectures—and protect against advanced adversaries, while giving customers the flexibility to balance cost and performance.”

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Elastic 8.1 Released

Elastic released Elastic 8.1.

New enhancements enable customers to stop advanced cyber threats with new prebuilt detections and data source integrations, and accelerate application development with deeper visibility into serverless architectures and continuous integration and continuous delivery (CI/CD) pipelines.

With enhanced end-to-end application performance monitoring visibility, customers can now collect traces from AWS Lambda, in beta, and correlate those traces with other Elastic Observability data—including from CI/CD environments—for faster and more comprehensive root cause analysis.

Additionally, support for OpenTelemetry logs, also in beta, enables organizations that use OpenTelemetry for traces and metrics to standardize data collection across all data types. The ability to ingest OpenTelemetry logs provides customers an opportunity to deploy a standardized, vendor-neutral observability architecture without losing correlation between signal types and layers.

Now generally available, the ability to enable doc-value-only fields gives customers the flexibility to index data faster while improving storage efficiency. With this new capability, customers can benefit from up to 20% faster indexing speeds and 20% lower data storage requirements, ultimately helping them accelerate time to insights while balancing cost and performance.

Customers can also leverage several new ad hoc analytics capabilities in Kibana Lens to enhance data exploration, including three new visualization types—gauge, waffle, and mosaic—and a new drag-and-drop capability to combine and compare multiple fields.

“As data volumes continue to grow and become more dispersed, cyber threats continue to rise,” said Santosh Krishnan, GM of Elastic Security, Elastic. “... Elastic offers faster indexing speeds, new prebuilt detections, and even more data source integrations to help analysts automate detection, improve prioritization, and accelerate threat analysis. These enhanced capabilities extend user visibility across digital ecosystems—including serverless architectures—and protect against advanced adversaries, while giving customers the flexibility to balance cost and performance.”

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In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...