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In Kubernetes version 1.9, the initial Alpha release of Container Storage Interface (CSI) was introduced. [70] Previously, storage volume plug-ins were included in the Kubernetes distribution. By creating a standardized CSI, the code required to interface with external storage systems was separated from the core Kubernetes code base.
Helm is a package manager for Kubernetes. [1] [2] It uses 'charts' as its package format, which is based on YAML. Helm was accepted to Cloud Native Computing Foundation on June 1, 2018 at the Incubating maturity level and then moved to the Graduated maturity level on May 1, 2020. [3]
Container clusters need to be managed. This includes functionality to create a cluster, to upgrade the software or repair it, balance the load between existing instances, scale by starting or stopping instances to adapt to the number of users, to log activities and monitor produced logs or the application itself by querying sensors.
Digital dashboard technology is available "out-of-the-box" from many software providers. Some companies, however, continue to do in-house development and maintenance of dashboard applications. For example, GE Aviation has developed a proprietary software/portal called "Digital Cockpit" to monitor the trends in the aircraft spare parts business.
Compute Engine – Infrastructure as a Service to run Microsoft Windows and Linux virtual machines. Google Kubernetes Engine (GKE) or GKE on-prem offered as part of Anthos platform [11] [12] – Containers as a Service based on Kubernetes. Cloud Functions – Functions as a Service to run event-driven code written in Node.js, Java, Python, or Go.
Kubeflow is an open-source platform for machine learning and MLOps on Kubernetes introduced by Google.The different stages in a typical machine learning lifecycle are represented with different software components in Kubeflow, including model development (Kubeflow Notebooks [4]), model training (Kubeflow Pipelines, [5] Kubeflow Training Operator [6]), model serving (KServe [a] [7]), and ...
Kibana provides visualization capabilities on top of the content indexed on an Elasticsearch cluster. Users can create bar, line and scatter plots, or pie charts and maps on top of large volumes of data.
Prometheus is not intended as a full-fledged dashboard. Although it can be used to graph specific queries, it is not a full-fledged dashboard and needs to be hooked up with Grafana to generate dashboards; this has been cited as a disadvantage due to the additional setup complexity. [32]