How to deploy a dockerized app to Microsoft Azure Web App for Containers

Deploying a Docker container on Azure ‘Web App for Containers’ can be done fairly easy. In this blog post, I will provide a step by step guide to get you started. Some basic knowledge of Azure and Docker definitely helps. But why should you care in the first place? You will get:

  • a managed runtime (for a single image)
  • scaling to multiple instances
  • a simple deployment model
  • easy integration with App Insights (Azure’s Monitoring system for Web Apps)
  • use any Azure SaaS like CosmosDB, MSSQL, …

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GuttenBase database migration framework 2.0.0 released

We’re glad to announce the new release of the GuttenBase database migration framework. The main but not only goal of this framework is to support database migrations between different (heterogenous) RDBMS, such as DB2, MySQL or Oracle. During the copying process you may apply various transformations such as data mapping, columns alteration, renaming tables, …

The 2.0 release features among various API enhancements and speed improvements: Java 8 support, a new tool to copy schemas between different databases, more supported database types, mapping of proprietary database column types, new documentation, …

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Why :focus-within is huge

Writing a post on any CSS selector or property (or even any new one coming up) would produce a long (and boring) blog. But :focus-within is different – and it’s something I have been waiting for forever. To me, it’s more than another pseudo-class, it’s a game-changer in many ways. And it’s in the stable versions of both Chrome and Firefox (and others). See the MDN page on :focus-within for a full (and esp. up2date) info on browser support.

What :focus-within does is quite simple, actually: it allows styling the parent(s) of the element that has the focus. Opposed to :focus (which references the element having the focus itself), it can be the direct parent, or the parent’s parent or the parent of the parent’s parent or… (you get it).
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Predicting house prices on Kaggle: a gentle introduction to data science – Part II

In Part I of this tutorial series, we started having a look at the Kaggle House Prices: Advanced Regression Techniques challenge, and talked about some approaches for data exploration and visualization. Armed with a better understanding of our dataset, in this post we will discuss some of the things we need to do to prepare our data for modelling. In particular, we will focus on treating missing values and encoding non-numerical data types, both of which are prerequisites for the majority of machine learning algorithms. We will briefly touch upon feature engineering as well – a crucial step for building effective predictive models. So let’s get started!
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Predicting house prices on Kaggle: a gentle introduction to data science – Part I

Data is ubiquitous these days, and being generated at an ever-increasing rate. However, left untouched and unexplored, it is of course of little use. This post will be the first in a series of tutorial articles exploring the process of moving from raw data to a predictive model. We’ll walk through the basic steps involved, and talk about some of the common pitfalls along the way.

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