Apache Flink might be a bit more popular than vert.x. We know about 27 links to it since March 2021 and only 26 links to vert.x. 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.
Https://vertx.io/ It's actively maintained with full time developers, performant, supports Kotlin out of the box, and has more features? - Source: Hacker News / 3 months ago
Hibernate Reactive integrates with Vert.x, but an extension allows to bridge to Project Reactor if wanted. - Source: dev.to / 10 months ago
Personally, I like vertx, it is modular and you can pick and choose what you need. It also has support for kotlin coroutines, https://vertx.io/, https://github.com/vert-x3/vertx-examples/tree/4.x/kotlin-examples. Source: about 1 year ago
I really like Eclipse Vert.x... As both an Erlang dev and Java dev, it's a great synergy and soon to have support for Virtual Threads similar to BEAM. Source: about 1 year ago
Eclipse Vert.x - Add amazing Async to any Java stack. Source: over 1 year ago
Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example,... - Source: dev.to / 15 days ago
Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 3 months ago
Also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc. - Source: dev.to / 4 months ago
Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features. - Source: dev.to / 4 months ago
Due to the technology transformation we want to do recently, we started to investigate Apache Iceberg. In addition, the data processing engine we use in house is Apache Flink, so it's only fair to look for an experimental environment that integrates Flink and Iceberg. - Source: dev.to / 4 months ago
Micronaut Framework - Build modular easily testable microservice & serverless apps
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Javalin - Simple REST APIs for Java and Kotlin
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
helidon - Helidon Project, Java libraries crafted for Microservices
Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.