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.
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...