Batch processing has been a challenging area of computer science since its inception in the early days of punch cards and magnetic tapes. By Mahmoud Ben Hassine. In this blog post, author introduces some of the challenges a batch developer or architect may face when designing and running batch applications at scale and show how Spring Batch, Spring Boot and Kubernetes can tremendously simplify this task
The main content:
- Fault tolerance
- Robustness
- Cost efficiency
- Observability
- Scalability
- How does Spring Batch make a batch developer’s life easier?
- How does Kubernetes make the batch operator’s life easier?
- Spring Batch on Kubernetes: a perfect match, in action
- Tips and Tricks
We are dealing with an unprecedented amounts of data, which is impossible to handle on a single machine any more. Correctly processing large volumes of distributed data is probably the most challenging point. Cloud-native batch applications should be scalable by design.
This post showed how to go from start.spring.io to Kubernetes in three simple steps, thanks to the productivity of the Spring ecosystem, but this is only scratching the surface of the matter. You will get full application example with all the code and kubernetes deployment files. Nice one!
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