https://github.com/trainingbypackt/cloud-native-applications-in-java-elearning

Build highly scalable microservice-based applications with Java for the cloud
https://github.com/trainingbypackt/cloud-native-applications-in-java-elearning

aws azure cloud-native cloud-native-java cqrs docker elasticsearch java java-microservices rest restful-api spring spring-cloud xaas

Last synced: 4 months ago
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Build highly scalable microservice-based applications with Java for the cloud

• Host: GitHub
• URL: https://github.com/trainingbypackt/cloud-native-applications-in-java-elearning
• Owner: TrainingByPackt
• License: mit
• Created: 2019-09-26T03:49:23.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2020-10-13T16:18:57.000Z (about 5 years ago)
• Last Synced: 2025-03-18T15:21:41.709Z (7 months ago)
• Topics: aws, azure, cloud-native, cloud-native-java, cqrs, docker, elasticsearch, java, java-microservices, rest, restful-api, spring, spring-cloud, xaas
• Language: Python
• Size: 27.6 MB
• Stars: 2
• Watchers: 2
• Forks: 4
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# Cloud-Native-Applications-in-Java

Cloud-native applications in java is your one-stop guide to building cloud-native applications in Java Spring that are hosted on AWS and Azure. This course teaches you everything you need to build secure, robust, and scalable microservice-based applications and deploy them into the cloud. 	 

## What you will learn

* Create Docker containers for microservices and set up continuous integration with Jenkins

* Monitor and troubleshoot applications in the cloud

* Use Docker and Kubernetes for containerization

* Perform log aggregation and visualization with the Elasticsearch, Logstash, and Kibana (ELK) stack

* Explore a variety of XaaS APIs and build your own XaaS model

* Migrate from a monolithic architecture to a cloud-native deployment

### Hardware requirements

For an optimal experience, we recommend the following hardware configuration:

* **Processor**: Intel i5 (or equivalent)

* **Memory**: 8GB RAM

* **Hard disk**: 10 GB

### Software requirements

You'll also need the following software installed in advance:

* Python 3.5+

  * Anaconda 4.3+

* Python libraries included with Anaconda installation:

  * matplotlib 2.1.0+

  * ipython 6.1.0+

  * requests 2.18.4+

  * beautifulsoup4 4.6.0+

  * numpy 1.13.1+

  * pandas 0.20.3+

  * scikit-learn 0.19.0+

  * seaborn 0.8.0+

  * bokeh 0.12.10+

* Python libraries that require manual installation:

  * mlxtend

  * version_information

  * ipython-sql

  * pdir2

  * graphviz