vert.x
Micronaut Framework
Javalin
helidon
Spark Framework
Netty
Akka
Apache Tomcat
Apache Beam
Google Cloud Dataflow
Google BigQuery
Snowflake
Qubole
Amazon EMR
Databricks
Apache Spark
Apache BeamBased on our record, vert.x should be more popular than Apache Beam. It has been mentiond 31 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.
Vert.x is the layer where Floci uses things directly. It's Netty with ergonomics: an event loop, a router, protocol-specific APIs for HTTP, DNS, TCP, WebSockets, gRPC, all sharing the same threading model. - Source: dev.to / 2 months ago
Traditionally, JDBC interfaces are all synchronous, so JdbcTemplate and HibernateTemplate are also synchronous. But as asynchronous high-concurrency programming spreads, reactive programming has entered mainstream frameworks. Spring now proposes the R2DBC standard, and the vertx framework includes asynchronous connectors for MySQL, PostgreSQL, etc. On the other hand, if an ORM engine acts as a data fusion access... - Source: dev.to / 9 months ago
The sixth release candidate of Eclipse Vert.x 5.0.0 provides support for the Java Platform Module System and a new VerticleBase class. Further details are available in the release notes. - Source: dev.to / over 1 year ago
I see your point, but I still don't think you can just say "If you want to get get a job as a Go developer, you must know gRPC." Even more so for Kafka, I've only heard about it being popular in the Java world. You can't even say "If you want to get a job as a Java developer, you must know Spring." Nowadays, sane Java projects use https://vertx.io, it's just too good. I would argue that Spring is for legacy... - Source: Hacker News / over 1 year ago
Vert.x is a toolkit for developing reactive applications on the JVM. I wrote a short introductory post about it earlier, when I used it for a commercial project. I had to revisit a Vert.x-based hobby project a few weeks ago, and I learned that there were some gaps in my knowledge about how Vert.x handles failures and errors. To fill those gaps, I did some experiments, wrote a few tests, and then wrote this blog post. - Source: dev.to / over 1 year ago
Use distributed data processing frameworks like Apache Beam or Apache Spark. - Source: dev.to / about 1 year ago
The "streaming systems" book answers your question and more: https://www.oreilly.com/library/view/streaming-systems/9781491983867/. It gives you a history of how batch processing started with MapReduce, and how attempts at scaling by moving towards streaming systems gave us all the subsequent frameworks (Spark, Beam, etc.). As for the framework called MapReduce, it isn't used much, but its descendant... - Source: Hacker News / over 2 years ago
Apache Beam is one of many tools that you can use. Source: over 2 years ago
Apache Beam: Streaming framework which can be run on several runner such as Apache Flink and GCP Dataflow. - Source: dev.to / over 3 years ago
Apache Beam: Batch/streaming data processing ๐Link. - Source: dev.to / almost 4 years ago
Micronaut Framework - Build modular easily testable microservice & serverless apps
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
Javalin - Simple REST APIs for Java and Kotlin
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
helidon - Helidon Project, Java libraries crafted for Microservices
Snowflake - Snowflake is the only data platform built for the cloud for all your data & all your users. Learn more about our purpose-built SQL cloud data warehouse.
