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Elastic Releases Elastic Stack 6.6 and APM on Elasticsearch Service

Elastic, the company behind Elasticsearch and the Elastic Stack, announced the general availability of version 6.6 of the Elastic Stack. This release delivers features that bring powerful operational efficiencies across several use cases, such as, logging, metrics, and APM.

Index lifecycle management and frozen indices simplify how users operationalize, automate, and organize their data as it comes in and as they want to make more data available for analyses.

Elasticsearch 6.6 also introduces geoshapes backed by the Bkd tree storage format, which brings higher storage densities and faster query speeds to geodata.

"We are excited to continue to deliver massive benefits to our users with the release of Elastic Stack 6.6, APM on Elasticsearch Service, and Elastic Cloud Enterprise 2.1," said Shay Banon, founder and CEO of Elastic. "Our new free, Basic subscription features like index lifecycle and frozen indices will improve the experience for users as they store and manage more data in Elasticsearch."

Elasticsearch users have always enjoyed a lot of control on how their data is indexed and stored to strike the right balance between performance and cost tradeoffs for their use case. Version 6.6 introduces foundational features into Elasticsearch that help users operationalize those choices over the lifecycle of their data. Index lifecycle management separates the lifecycle of an index into four phases -- hot, warm, cold, and delete -- and lets the user define and automate policies to control how long an index should live any phase, and the set of actions (for example, move data from hot node to cold node) to be taken on the index during each phase. Index lifecycle management is available as a free, Basic subscription feature.

Elasticsearch 6.6 also introduces frozen indices that dramatically reduce memory requirements (and by extension hardware specs), and support more cost-effective sizing recommendations for long-term data retention needs. Frozen indices can be queried just like open indices, but trade off query speeds for a lower memory footprint. Users are always looking for new dimensions and techniques to manage hardware spend, and frozen indices give these users one more tool in the toolbox to reduce their memory footprint and hardware costs. Frozen indices is available as a free, Basic subscription feature.

Continuing in the storage and performance theme, Elasticsearch 6.6 introduces Bkd-backed geoshapes, resulting in significant storage and performance improvement when querying geoshape data. This feature brings the benefits of the more efficient Bkd tree based storage format to more data types, and by extension to additional use cases. Bkd-backed geoshapes is available as an Elastic Stack open source feature.

Elastic has always provided users with a choice on how they consume Elastic technology, and recent releases extend that choice to Elastic APM. Elastic users can now add Elastic APM to their deployments running in Elasticsearch Service or Elastic Cloud Enterprise. Distributed tracing was introduced as a beta feature in Elastic APM version 6.5 and is now also generally available as a free, Basic subscription feature. Additionally, Elastic APM is now OpenTracing compatible.

Other significant developments include:

- Preconfigured machine learning jobs for Auditbeat data to detect unusual events in audit data, especially security-related anomalies.

- Elastic Cloud Enterprise 2.1 launches with support for cross-cluster search, IP filtering, and other 6.6 goodies you will see today.

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Elastic Releases Elastic Stack 6.6 and APM on Elasticsearch Service

Elastic, the company behind Elasticsearch and the Elastic Stack, announced the general availability of version 6.6 of the Elastic Stack. This release delivers features that bring powerful operational efficiencies across several use cases, such as, logging, metrics, and APM.

Index lifecycle management and frozen indices simplify how users operationalize, automate, and organize their data as it comes in and as they want to make more data available for analyses.

Elasticsearch 6.6 also introduces geoshapes backed by the Bkd tree storage format, which brings higher storage densities and faster query speeds to geodata.

"We are excited to continue to deliver massive benefits to our users with the release of Elastic Stack 6.6, APM on Elasticsearch Service, and Elastic Cloud Enterprise 2.1," said Shay Banon, founder and CEO of Elastic. "Our new free, Basic subscription features like index lifecycle and frozen indices will improve the experience for users as they store and manage more data in Elasticsearch."

Elasticsearch users have always enjoyed a lot of control on how their data is indexed and stored to strike the right balance between performance and cost tradeoffs for their use case. Version 6.6 introduces foundational features into Elasticsearch that help users operationalize those choices over the lifecycle of their data. Index lifecycle management separates the lifecycle of an index into four phases -- hot, warm, cold, and delete -- and lets the user define and automate policies to control how long an index should live any phase, and the set of actions (for example, move data from hot node to cold node) to be taken on the index during each phase. Index lifecycle management is available as a free, Basic subscription feature.

Elasticsearch 6.6 also introduces frozen indices that dramatically reduce memory requirements (and by extension hardware specs), and support more cost-effective sizing recommendations for long-term data retention needs. Frozen indices can be queried just like open indices, but trade off query speeds for a lower memory footprint. Users are always looking for new dimensions and techniques to manage hardware spend, and frozen indices give these users one more tool in the toolbox to reduce their memory footprint and hardware costs. Frozen indices is available as a free, Basic subscription feature.

Continuing in the storage and performance theme, Elasticsearch 6.6 introduces Bkd-backed geoshapes, resulting in significant storage and performance improvement when querying geoshape data. This feature brings the benefits of the more efficient Bkd tree based storage format to more data types, and by extension to additional use cases. Bkd-backed geoshapes is available as an Elastic Stack open source feature.

Elastic has always provided users with a choice on how they consume Elastic technology, and recent releases extend that choice to Elastic APM. Elastic users can now add Elastic APM to their deployments running in Elasticsearch Service or Elastic Cloud Enterprise. Distributed tracing was introduced as a beta feature in Elastic APM version 6.5 and is now also generally available as a free, Basic subscription feature. Additionally, Elastic APM is now OpenTracing compatible.

Other significant developments include:

- Preconfigured machine learning jobs for Auditbeat data to detect unusual events in audit data, especially security-related anomalies.

- Elastic Cloud Enterprise 2.1 launches with support for cross-cluster search, IP filtering, and other 6.6 goodies you will see today.

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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 ...