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Grafana Labs works everyday to break traditional data boundaries with metric-visualization tools accessible across entire organizations. It began as a pure open-source project and has since expanded into supported subscription services. The Grafana open-source project is a platform for monitoring and analyzing time series data. There are also subscription offerings such as the supported Grafana Enterprise version. Grafana Labs’ engineers service more than 150,000 active installations. Users include companies such as PayPal, eBay and Booking.com.

HashiCorp Consul is agent-based cluster management software that addresses the challenge of sharing network and configuration details across a distributed system. Consul handles service discovery and configuration for potentially massive clusters of hosts, spread across multiple datacenters. Consul was released in 2014, and organizations have adopted it for its service discovery capabilities, distributed key-value store, and automated health checks, among other features (including, recently, a service mesh).

A recent CNCF-sponsored Kubernetes security audit uncovered CVE-2019-11246, a high-severity vulnerability affecting the command-line kubectl tool. If exploited, it could lead to a directory traversal, allowing a malicious container to replace or create files on a user’s workstation. This vulnerability stemmed from an incomplete fix of a previously disclosed vulnerability (CVE-2019-1002101). Source: sysdig.com

Kubernetes image scanning Kubernetes runtime security Kubernetes network security Image distribution and secrets management Kubernetes security audit End-to-end commercial security tools Join our live session to learn more! Kubernetes security tools … there are so freaking many of them; with different purposes, scopes and licenses. That’s why we decided to create this Kubernetes security tools list, including open source projects and commercial platforms from different vendors, to help you choose the ones that look more interesting to you and guide you in the right direction depending on your Kubernetes security needs.

One of the core goals when modernising software systems is to decouple applications from the underlying infrastructure on which they are running. This can provide many benefits, including: workload portability, integration with cloud AI/ML services, reducing costs, and improving/delegating specific aspects of security. The use of containers and orchestration frameworks like Kubernetes can decouple the deployment and execution of applications from the underlying hardware.

Hello all, Leigh Estes, aka RiotSchmick, here. I’m a software engineer at Riot Games working on the Riot Developer Experience team. Our responsibilities include providing the edge infrastructure that supports both internal and external developers. I previously wrote a series on the infrastructure that supports our public API product. I’m excited to revisit this series to tell you more about a new part of our infrastructure – the feature we call transforms. I’ll outline the reasons that we felt transforms were a valuable feature to invest in and how we implemented them.

Service meshes add a lot of functionality to application deployments, including traffic policies, observability, and secure communication. But adding a service mesh to your environment comes at a cost, whether that’s time (added latency) or resources (CPU cycles). To make an informed decision on whether a service mesh is right for your use case, it’s important to evaluate how your application performs when deployed with a service mesh.

Deriving information and insights from data in the Uber marketplace requires analyzing data across an entire city. Grid systems, which partition geographic areas into identifiable cells and facilitate the exploration of data at a fine granularity, are critical to this effort. When Uber designed a grid system to bucket geospatial events, we looked to the hexagon, a shape that enabled us to minimize quantization error and easily approximate radiuses.

Variables in Grafana (previously known as templates) allow parameterisation of a dashboard via a drop-down menu. Often this is used to switch between machines or services, so that you can have per-machine dashboards without needing to create a dashboard every time a new machine appears. They’re also stored in URL parameters, so could be linked from alert notifications or wiki pages.

Neural networks are widely used in complex tasks such as machine translation, image classification, or speech recognition. These networks are data driven, and as the amount of data increases so does network size and the computational complexity required for training and inference. Recently, Facebook AI Research (FAIR) researchers introduced a structured memory layer which can be easily integrated into a neural network to greatly expand network capacity and the number of parameters without significantly changing calculation cost.