Post Overview

  • IoT with Azure Database for PostgreSQL and TimescaleDB

    IoT with Azure Database for PostgreSQL and TimescaleDB

    In IoT projects, the same question always comes up: “Where do we store our telemetry data?”. As usual, the answer to that question is not straightforward. We have seen all kinds of solutions in the wild: save directly to a relational database (SQL Server, MySQL, …) save to a data lake and/or SQL save to Read more

  • Cloud Run on Google Kubernetes Engine

    Cloud Run on Google Kubernetes Engine

    In this short post, we will take a look at Cloud Run on Google Kubernetes Engine (GKE). To get this to work, you will need to deploy a Kubernetes cluster. Make sure you use nodes with at least 2 vCPUs and 7.5 GB of memory. Take a look here for more details. You will notice Read more

  • Trying Google Cloud Run

    With the release of Google’s Cloud Run, I decided to check it out with my nasnet container. With Cloud Run, you simply deploy your container and let Google scale it based on the requests it receives. When your container is not used, it gets scaled to zero. As such, it combines the properties of a Read more

  • Azure Front Door Revisited

    A while ago, I wrote a post about Azure Front Door. In that post, I wrote that http to https redirection was not possible. With Azure Front Door being GA, let’s take a look if that is still the case. In the previous post, I had the following configuration in Front Door Designer: The above Read more

  • Hosting an Angular app in Kubernetes

    Hosting an Angular app in Kubernetes

    We recently had to deploy an Angular application to Kubernetes in three different environments: development, acceptance and production. The application is not accessed via the browser directly. Instead, it’s accessed via a Microsoft Office add-in. The next sections will provide you with some tips to make this work. In practice, I do not recommend hosting Read more

  • Creating and containerizing a TensorFlow Go application

    Creating and containerizing a TensorFlow Go application

    In an earlier post, I discussed using a TensorFlow model from a Go application. With the TensorFlow bindings for Go, you can load a model that was exported with TensorFlow’s SavedModelBuilder module. That module saves a “snapshot” of a trained model which can be used for inference. In this post, we will actually use the Read more