The Apache Hadoop platform is widely used for big data processing. Spring Boot is a popular Java-based framework for developing microservices.
In this post, we'll see how to integrate Spring Boot with Apache Hadoop. We'll learn about the benefits of doing so and some of the challenges that you may face.
There are several reasons why you might want to integrate Spring Boot with Apache Hadoop.
One reason is that Spring Boot makes it easy to create stand-alone, production-grade microservices. This can be a great benefit when you're dealing with big data, as it can help you to process data more quickly and efficiently.
Another reason is that Spring Boot can help to make your Apache Hadoop cluster more manageable. For example, Spring Boot can help you to dynamically configure your Hadoop cluster, making it easier to scale up or down as needed.
One challenge you may face when integrating Spring Boot with Apache Hadoop is that the two platforms use different versions of Java. Spring Boot requires Java 8, while Apache Hadoop generally uses an older version of Java (usually Java 7).
This means that you'll need to use a version of Hadoop that's compatible with Java 8, or you'll need to configure Spring Boot to use an older version of Java.
Another challenge is that Spring Boot applications are typically deployed on application servers, such as Apache Tomcat or Jetty. However, Hadoop is designed to run on a cluster of commodity hardware, without the need for an application server.
This means that you'll need to configure Hadoop to run on a cluster of application servers, or you'll need to deploy your Spring Boot application on a Hadoop cluster.
In this post, we've seen how to integrate Spring Boot with Apache Hadoop. We've also seen some of the challenges that you may face when doing so.
If you're considering integrating Spring Boot with Apache Hadoop, be sure to carefully consider the benefits and challenges before making a decision.