Erynis observability stack

EACH VESSEL MUST CONDUCT CONTINUOUS AND RESPONSIBLE VISUAL AND AUDITORY OBSERVATIONS, DEPENDING ON THE PREVAILING CIRCUMSTANCES AND CONDITIONS.

 

 

PROCESSING OF APPLICATION AND INFRASTRUCTURE METRICS

Observability technologies help to understand the internal events and processes of applications, their components, and platforms. Nowadays widely adopted distributed approach to application architecture does not make this any simpler, rather the opposite. Typically, micro-service architecture involves an enormous amount of different technologies whose pace of change is breathtaking. To keep an understanding of how these systems work under different circumstances is crucial to avoid critical outages. Therefore, ELOS Erynis is here to help with this.

Features

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ELOS ERYNIS observability stack

ELOS Erynis: a unified solution for logs, metrics, and tracing that scales with your applications and infrastructure. Helps to observe the application and/or infrastructure components regardless of type: Kubernetes or OpenShift resources, traditional VMs, bare-metal, network devices, storage, etc. ELOS Erynis is a perfect fit for hybrid environments, it natively supports multi-cluster and hybrid-cluster scenarios, gives your teams a single location to query and analyze all the collected data, no matter if they need it for DevOps, maintenance, or compliance reasons. Built on widely adopted open source technologies Prometheus, Thanos, Grafana that are carefully integrated and tested. ELOS Erynis is focused on critical enterprise environments, designed for manageability and resiliency, accompanied by a wide range of professional services and support.

 

 

Read more in our blog

Metrics visualization

Parallel collection of metrics from containerized and traditionally operated applications, infrastructure components, middleware, etc. brings a number of possibilities. Visualizing the relationships between them can reveal a number of problems that may not be obvious at first glance.

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Collection and correlation of metrics from multiple sources

In a typical customer environment, we often solve a similar problem: we need to collect metrics for applications running in many Kubernetes clusters in different environments, and we need to correlate them with metrics for various infrastructure components, such as storage or load balancers.

 

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