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Based on our record, Materialize seems to be a lot more popular than Apache ActiveMQ. While we know about 72 links to Materialize, we've tracked only 7 mentions of Apache ActiveMQ. 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.
Before Kafka, traditional message queues like RabbitMQ and ActiveMQ were widely used, but they had limitations in handling massive, high-throughput real-time data streams. - Source: dev.to / 2 months ago
Consume open-source queuing services – customers can deploy message brokers such as ActiveMQ or RabbitMQ, to develop asynchronous applications, and when moving to the public cloud, use the cloud providers managed services alternatives. - Source: dev.to / 2 months ago
Apache ActiveMQ is an open-source Java-based message queue that can be accessed by clients written in Javascript, C, C++, Python and .NET. There are two versions of ActiveMQ, the existing “classic” version and the next generation “Artemis” version, which is currently being worked on. - Source: dev.to / almost 2 years ago
For real-time streaming, we have other frameworks and tools like Apache Kafka, ActiveMQ, and AWS Kinesis. - Source: dev.to / over 2 years ago
The back-end is designed as a set of microservices communicating through a message broker, ActiveMQ, with a custom configuration to support delayed delivery and other features. - Source: dev.to / almost 3 years ago
It's hard to write something that is both accessible and well-motivated. The best uses of category theory is when the morphisms are far more exotic than "regular functions". E.g. It would be nice to describe a circuit of live queries (like https://materialize.com/ stuff) with proper caching, joins, etc. Figuring this out is a bit of an open problem. Haskell's standard library's Monad and stuff are watered down to... - Source: Hacker News / 5 months ago
> [...] `https://materialize.com/` to solve their memory issues [...] Disclaimer: I work at Materialize Recently there have been major improvements in Materialize's memory usage as well as using disk to swap out some data. I find it pretty easy to hook up to Postgres/MySQL/Kafka instances: https://materialize.com/blog/materialize-emulator/. - Source: Hacker News / 5 months ago
I agree. So many disparate solutions. The streaming sql primitives are by themselves good enough (e.g. `tumble`, `hop` or `session` windows), but the infrastructural components are always rough in real life use cases. Crossing fingers for solutions like `https://github.com/feldera/feldera` to solve their memory issues, or `https://clickhouse.com/docs/en/materialized-view` to solve reliable streaming consumption.... - Source: Hacker News / 5 months ago
Or the related Materialize stuff https://materialize.com/. - Source: Hacker News / 6 months ago
The original post makes so much more sense in this context! One of the "holy grails" in my mind is making CQRS and dataflow programming as easy to learn and maintain as existing imperative programming languages - and easy to weave into real-time UX. There are so many backend endpoints in the wild that do a bunch of things in a loop, many of which will require I/O or calls to slow external endpoints, transform the... - Source: Hacker News / 7 months ago
RabbitMQ - RabbitMQ is an open source message broker software.
RisingWave - RisingWave is a stream processing platform that utilizes SQL to enhance data analysis, offering improved insights on real-time data.
IBM MQ - IBM MQ is messaging middleware that simplifies and accelerates the integration of diverse applications and data across multiple platforms.
Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
Apache Kafka - Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.
Amazon SQS - Amazon Simple Queue Service is a fully managed message queuing service.