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At Banzai Cloud we are passionate about observability, and we expend a great amount of effort to make sure we always know what’s happening inside our Kubernetes clusters. All clusters provisioned with Pipeline - our multi- and hybrid-cloud container management platform - are provided with, and rely upon, each of the three pillars of observability: federated monitoring, centralized log collection and traces. In order to automate log collection on Kubernetes, we opensourced a logging-operator built on the Fluent ecosystem.

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This is the second part of a very popular post, Helm from basics to advanced. In the previous post (we highly suggest you read it, if you haven’t done so already) we covered Helm’s basics, and finished with an examination of design principles. In this post, we’d like to continue our discussion of Helm by exploring best practices and taking a look at some common mistakes. If you are looking for a place to securely store your Helm charts, remember that Banzai Cloud runs a free Helm Charts repository as a service: charts.

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Logs (one of the three pillars of observability besides metrics and traces) are an indispensable part of any distributed application. Whether we run these applications on Kubernetes or not, logs are one of the best ways to diagnose and verify an application state. One of the key features of our Kubernetes platform, Pipeline, is to provide out-of-the-box metrics, trace support and log collection. This post highlights some of the behind the scenes automation we’ve constructed in order to achieve this.

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Banzai Cloud is on a mission to simplify the development, deployment, and scaling of complex applications and to bring the full power of Kubernetes to all developers and enterprises. Banzai Cloud’s Pipeline provides a platform which allows enterprises to develop, deploy and scale container-based applications. It leverages best-of-breed technology from the Cloud Native Foundation ecosystem to create a highly productive, yet flexible environment for developers and operation teams alike. One of the key tools we use from the Kubernetes ecosystem is Helm.

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Jun 25 2018

Hands on Thanos

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Here at Banzai Cloud we blog a lot about Prometheus and how to use it. One of the problems we have so far neglected to discuss is the inadequate long term storage capability of Prometheus. Luckily a new project called Thanos seeks to address this. If you are not familiar with Prometheus, or are interested in other monitoring related articles, check out our monitoring series, here: Monitoring series: Monitoring Apache Spark with Prometheus Monitoring multiple federated clusters with Prometheus - the secure way Application monitoring with Prometheus and Pipeline Building a cloud cost management system on top of Prometheus Monitoring Spark with Prometheus, reloaded

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In this blog we’ll continue our series about Kubernetes logging, and cover some advanced techniques and visualizations pertaining to collected logs. Just to recap, with our open source PaaS, Pipeline, we monitor and collect/move a large number of the logs for the distributed applications we push to Kubernetes. We are expending a lot of effort to monitor large and federated clusters, and to automate these with Pipeline, so that our users receive out of the box monitoring and log collection for free.

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As part of the Debug 101 series, we’re back hunting a small but annoying bug. This kind of bug is not really a bug, but a side effect of several tools working together. Here comes trouble I deploy a development version of Pipeline on a Kubernetes cluster running on top of AWS infrastructure. For this deployment I use the following Helm chart command. $: helm install --name pipeline banzaicloud-stable/pipeline-cp \ --set=drone.

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Monitoring series: Monitoring Apache Spark with Prometheus Monitoring multiple federated clusters with Prometheus - the secure way Application monitoring with Prometheus and Pipeline Building a cloud cost management system on top of Prometheus Monitoring Spark with Prometheus, reloaded At Banzai Cloud we provision and monitor large Kubernetes clusters deployed to multiple cloud/hybrid environments. These clusters and applications or frameworks are all managed by our next generation PaaS, Pipeline.

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