Category Archives: Java Runtimes – VM, Appserver & Cloud

WSL 2: The new Windows Subsystem for Linux

This post gives a general introduction with focus on the new version WSL 2. It provides some basic usage principles and outlines some advantages and disadvantages. In future posts to come, I will evaluate using WSL 2 as a software … Continue reading

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Re-deploy your containerized Java application on Azure

In my previous post a while ago I showed you how to deploy your dockerized Java application on Azure. It was a step-by-step introduction switching from browser to IDE, to command line (Docker CLI or Azure CLI), to the Azure … Continue reading

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Was ist der Unterschied zwischen RDD, DataFrame und Dataset in Apache Spark?

Apache Spark wurde entwickelt, um eine höhere Leistung im Hadoop-Ökosystem erreichen zu können. Im Vergleich zu Hadoop soll Spark dank In-Memory-Berechnungen und weiterer Optimierungen bei der Datenverarbeitung wesentlich schneller sein. In diesem Blog will ich erklären, welche Datenstrukturen Spark mitbringt und … Continue reading

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Möglichkeiten, eine Spring-Anwendung auf der Azure Cloud zu betreiben

Auf Azure gibt es unzählige Möglichkeiten, eine Java Anwendung zu betreiben. In diesem Artikel möchte ich anhand eines Beispiels vier verschiedene Methoden zeigen, wie man eine Spring Boot Applikation auf Microsoft Azure deployen kann.

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How to run a containerized Java application in the cloud on Microsoft Azure

My intention for this blog post was to see how fast could I, with basically zero practical cloud experience, deploy a Java application in just that. For this purpose I decided to go for the Azure Cloud Services. Additionally, I … Continue reading

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High performance at low cost – choose the best JVM and the best Garbage Collector for your needs

The Garbage Collector (GC) is an integral part of every Java Virtual Machine (JVM) – running in the Cloud, on a dedicated server or on your desktop/laptop. Are you aware of what impact a GC has on an application – … Continue reading

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Map Reduce in Hadoop Example

Hadoop MapReduce is a software framework used for distributed processing of large data sets on compute clusters. According to The Apache Software Foundation, the objective of Map/Reduce is to split the input data set into independent chunks that are processed … Continue reading

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