Micronaut Framework
vert.x
Quarkus
Javalin
helidon
Node.js
Spark Framework
GatsbyJS
Hadoop
Apache Spark
Apache Cassandra
Apache Flink
PostgreSQL
MongoDB
Apache Storm
Apache Kafka
Micronaut Framework HadoopBased on our record, Micronaut Framework should be more popular than Hadoop. It has been mentiond 36 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Micronaut has a share of the space too. https://micronaut.io/ However, you’re right that Spring Boot has the lions share of the Java ecosystem. - Source: Hacker News / 3 months ago
I've used vert.x in a big project once. I don't ever want to do that again. Performance is pretty good, but the developer experience is beyond clunky. My current favourite Java server framework is Micronaut. Great performance and easy to develop for! https://micronaut.io/. - Source: Hacker News / 3 months ago
One of the most common web frameworks used is Spring Boot - here is their quickstart: https://spring.io/quickstart Newer alternatives are: https://micronaut.io/ and https://quarkus.io/ If you want to have something really simple look at Javalin: https://javalin.io/. - Source: Hacker News / 8 months ago
I wonder how much you'd save with Micronaut: https://micronaut.io/ > Micronaut is a software framework for the Java virtual machine platform. It is designed to avoid reflection, thus reducing memory consumption and improving start times. Features which would typically be implemented at run-time are instead pre-computed at compile time. https://en.wikipedia.org/wiki/Micronaut_(framework) I don't think you'd go down... - Source: Hacker News / 10 months ago
I'd like to introduce my project. It is called mlfx. It can compile FXML ahead of time. It is basically an annotation processor, which internally uses Micronaut framework's AST abstraction and compiles fxml files directly to JVM bytecode. This decreases UI load time and also helps with native-image reflection configs. It also has some compliance tests that load compiled code and Check resulting object graph... Source: 12 months ago
Did you check out tools like https://hadoop.apache.org/ ? Source: about 1 year ago
There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: about 1 year ago
There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS. - Source: dev.to / over 1 year ago
A copy of Hadoop installed on each of these machines. You can download Hadoop from the Apache website, or you can use a distribution like Cloudera or Hortonworks. - Source: dev.to / over 1 year ago
The Apache™ Hadoop™ project develops open-source software for reliable, scalable, distributed computing. - Source: dev.to / over 1 year ago
vert.x - From Wikipedia, the free encyclopedia
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Quarkus - Quarkus: Supersonic Subatomic Java. . Contribute to quarkusio/quarkus development by creating an account on GitHub.
Apache Cassandra - The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance.
Javalin - Simple REST APIs for Java and Kotlin
Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.