Based on our record, ZeroMQ should be more popular than Amazon EMR. It has been mentiond 35 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.
In this post from 2011, the creator of Omegle, Leif Brooks, explains what technology is used, including Python and a library called gevent for the backend. On top of that, Adobe Cirrus is used for streaming video. Though this post was 12 years ago, it is valuable to know what a web application like Omegle requires. A modern library that may provide some functionality for a text chat at a minimum may be... Source: 6 months ago
They might be thinking of something like ZeroMQ, which is pretty well liked: https://zeromq.org/ That said, I wouldn't call RabbitMQ that heavyweight myself, at least when compared to something like Apache Kafka. - Source: Hacker News / 7 months ago
If you want to learn message passing in an environment you're familiar with, you should check out ZeroMQ. It's a C++ lib for socket abstraction, it's immensely useful in distributed systems, it can also do in-process message passing, and it's got bindings/ports for C and Rust. Source: 12 months ago
Inspired by the IDE language server protocol, I created an API interface between the electron and the Python ML interface. ZeroMQ turned out be an invaluable resource as a fast and lightweight messaging queue between the two. - Source: dev.to / about 1 year ago
If you really need it live, like for a chat or auctions you can use https://zeromq.org/ over websockets. 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
I'm going to guess you want something like EMR. Which can take large data sets segment it across multiple executors and coalesce the data back into a final dataset. Source: almost 2 years ago
This is exactly the kind of workload EMR was made for, you can even run it serverless nowadays. Athena might be a viable option as well. Source: almost 2 years ago
Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce). - Source: dev.to / over 2 years ago
Check out https://aws.amazon.com/emr/. Source: about 2 years ago
RabbitMQ - RabbitMQ is an open source message broker software.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
Apache Kafka - Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
Apache ActiveMQ - Apache ActiveMQ is an open source messaging and integration patterns server.
Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost