Data – specifically time series data – continues to be the key ingredient for successful digital transformation. No matter the industry, time series data helps companies understand the activities and output of people, processes and technologies impacting their business. The effective management and use of time series data has emerged as the best path towards this goal.

Kubernetes has experienced rapid growth over the years, with a recent post from the Cloud Native Computing Foundation reporting a userbase increase of about 67% in just the past year. Kubernetes is a container orchestration platform that automates how containers are deployed, how they communicate, and how traffic is routed between them; it also scales configurations for both the containerized workloads and the underlying infrastructure that comprises the cluster.

InfluxData today announced accelerated momentum in Industrial Data and Internet of Things (IoT) driven by new customers, product enhancements and expanded industrial partnerships fueled by the growth of time series data. Customers including Tesla, Rolls Royce, Airbus, Teck, Graco and Graphite Energy are using InfluxDB to collect industrial data from devices and sensors.

One major challenge with decarbonization of industrial heat is converting the variability of renewable energy into the reliability required by process plants. Solar panels only generate energy when the sun is out, and wind turbines generate energy when the wind blows. Industry, however, has a consistent and persistent need for energy. Graphite Energy, based in Australia, recognized this disconnect and set out to create a solution to it.

It is safe to say that customers and enterprises have come to expect their digital experiences to be near instantaneous. Fifty three percent of consumers will wait no more than three seconds for a web page to render before abandoning the site. But new technologies, like connected vehicles, AR/VR, and industrial automation, are pushing the limits of what traditional architecture can handle when it comes to delivering ultra-low latency.

Welcome to the concluding chapter of this three-part blog series on the low latency Ubuntu kernel for industrial embedded systems. Each blog is standalone and can be read independently from the others, although you may want to start at the beginning for some continuity. If you need a quick refresher on userland and kernel space, we recommend you check Part I out first.

In today’s world, graphics processing units or GPUs have attracted a lot of attention as the optimal vehicle to run artificial intelligence (AI), machine learning (ML) and deep learning (DL) workloads. These workloads require massive amounts of data, both ultra-high speed and parallel processing, along with flexibility and high availability. It is clear that high-performance computing (HPC) with graphics processing unit (GPU) systems are required to support cutting-edge workloads.