Monitoring Kubernetes and Docker just got easier thanks to Sumo Logic
|Christian Hargrave in Cloud Services Tuesday, June 19, 2018|
Sumo Logic launches new container based platform features they say will help customers facilitate faster innovation, deliver better customer experiences and increase decision-making power across their organizations.
Sumo Logic has new platform capabilities that aim to make it easier to automate and manage application architectures that use containers, such as Docker, and orchestration software, like Kubernetes and Amazon EKS.
Modern applications are driving digital services that are reliant on continuous improvement practices to ensure their customers’ experiences remain engaging, sticky and secure. However, microservices-based architectures are highly distributed, making them harder to effectively monitor and manage, which opens them up to operations and security risks that can disrupt or undermine customer experience. Sumo Logic’s new platform enhancements now provide richer metadata support for instrumentation, analytics and cloud platforms, and convert unstructured data to structured and time series data in real-time, allowing users to extract more business value from their machine data faster to speed application improvement cycles, deliver better customer experiences and increase decision-making across the organization.
Sumo Logic now supports Kubernetes wherever it runs with native support for Amazon Elastic Container Service for Kubernetes (Amazon EKS) new managed service. Additionally, Sumo Logic has added support for performance metrics and metadata via the open source standard Prometheus, used by the Kubernetes community. This allows customers to ingest metrics and metadata relevant to monitoring Kubernetes clusters, to quickly and proactively resolve customer issues and reduce downtime.
Unstructured machine data is not always optimized for the kind of real-time analytics customers need to inform business decisions. Now with Sumo Logic, customers can extract performance metrics and performance indicators from unstructured logs, while still retaining those logs for root cause analysis. These metrics can then be used with the Sumo Logic time series engine, providing 10 to 100 times the analytics performance improvements over unstructured log data searches, as well as support long-term trending of metrics.
Increasing the accessibility of essential machine data insights and mapping those to actionable, contextual business analytics for IT and non-core-IT users is critical. In addition to full metadata support for Kubernetes, as mentioned above, users can now convert their existing Graphite-formatted performance metrics into the metadata-rich, metrics 2.0 format supported by Sumo Logic, both in real-time and after ingestion. This allows customers to increase the usability and accessibility for their analytics users by allowing them to leverage business relevant tags, instead of relying only on obscure, technical tags. Sumo Logic has also unveiled custom tagging support for any log source.
“The world is moving from generic solutions to personalized ones that address very specific customer pain points, and this requires an agile and flexible platform built for the cloud,” said Bruno Kurtic, founding VP of product and strategy, Sumo Logic. “Legacy analytics tools have failed organizations because they can no longer deliver the visibility needed to support the investment customers are making in modern architectures at cloud scale. The new enhancements to Sumo Logic’s platform not only provide real-time access to machine data analytics as a service but also make data easily accessible to everyone, enabling organizations to leverage these insights to drive better experiences for their customers.”
Clouds are distributed technology platforms that leverage sophisticated technology innovations to provide highly scalable and resilient environments that can be remotely utilized by organizations in a multitude of powerful ways. To successfully build upon, integrate with, or even create a cloud environment requires an understanding of its common inner mechanics, architectural layers, and models, as well as an understanding of the business and economic factors that result from the adoption and real-world use of cloud-based services.
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