This video livestream meetup by Netdata takes a deep dive into the fundamentals of Machine Learning in DevOps Infrastructure Monitoring. It also covers the Netdata way of approaching Machine Learning. The Anomaly Advisor major update to Netdata is introduced as a valuable troubleshooting tool for any DevOps or Site Reliability Engineer looking for anomalies in their infrastructure. The hosts share real-world infrastructure monitoring & troubleshooting examples, as well as early feedback from the community on the Anomaly Advisor.

Robert Sirchia host a special guest Glen Kosaka to talk about the NeuVector 5.0 release.

Docker Bangalore & JFrog Bangalore celebrate Docker's 9th Birthday

There is a yawning gap opening up between the best and the rest — the elite top few percent of engineering teams are making incredible gains year on year in velocity, reliability and human compatibility, whilst the bottom 50% are actually losing ground. The loss has nothing to do with engineering ability. Take an engineer out of an elite-performing team and place them in the bottom 50%, and they become subpar too; take an engineer out of a mediocre team and embed them in an elite team, and they are pulling their weight within the year.

Believe it or not, the GitOps paradigm is now going on year five since the defining piece from WeaveWorks. So, at least in theory, leveraging a declarative approach that pairs Kubernetes with GitOps pillars is not novel at this point. But the trickier question right now – and one open to no shortage of interpretation – is what you are sending to a GitOps Engine. The multiple paths of creating Kubernetes manifests and packaging (like Helm, Kustomize, JSonnett, etc) are largely leaving us to our own devices for abstraction. Is an app an app if there is no longer a templating engine?

As organizations have moved from monolithic to microservice-based architectures, there has been an explosion in the volume of logs generated. Most logging solutions create a full index of the logs and use SSD drives, which results in costly compute and storage resources for logs that are mostly write once, read never. We created Grafana Loki to solve these problems. Loki only indexes the metadata of the log lines, relies on inexpensive object storage, and is architected for scalability. In addition, Loki takes advantage of parallelism and sharding that results in fast query performance. In this session, we will discuss the benefits of using Loki as a backend logging solution.

Cardinality Analysis of metrics is an enabler to reducing costs and focusing observability on the necessary metrics to identify and investigate where issues are occurring in your services. Grafana has added cardinality management dashboards to Grafana Cloud and Grafana Enterprise Metrics to make this an easy and fast process. In this introductory session, we will provide an overview of the Grafana Cardinality features and offer a set of discovery questions to help you with the process.

Richardo Rocha, Computing Engineer at CERN, joined the Meetup for Argo monthly series and talked about managing Multi-Cluster, Multi-Cloud Deployments with GitOps and ArgoCD. The last few years saw two relevant trends in CERN deployments: Kubernetes and cloud-native technologies on the deployment side, and public clouds as a potentially cost-effective way to get the additional capacity to cover workloads spikes and access resources that are scarce on-premises, such as GPUs and other accelerators.

Join Grafanistas Jessica Brown, Marcus Olsson, and Mat Ryer as they present at the April 2022 Grafana Labs Virtual EMEA Meetup. Here's a brief look at their talks: “Extend your Grafana experience through plugins” By default, Grafana comes with an impressive set of different visualizations and data source integrations. But that’s not all! Many more panels and data sources are available as plugins, built by the Grafana community. In this session, you’ll learn about the new in-app plugin catalog and a few nifty plugins to get you started using plugins!

Serverless technologies offer a great foundation for building resilient applications that can withstand much turbulence in the production environment. For instance, AWS Lambda automatically deploys your code to three availability zones and replaces faulty virtual machines on the fly. Despite this, there are still many other types of failures that can still affect our application. Perhaps there is an outage with a third-party service we depend on, or maybe a sudden surge in throughput has pushed us over the throughput limit and caused some user requests to be throttled.