Apache ActiveMQ is recommended for enterprises looking for a reliable and scalable message broker, developers needing rich messaging functionality, and organizations that require robust support for various messaging protocols, including JMS, AMQP, STOMP, and MQTT. It is particularly well-suited for applications that need to distribute messages between different applications, languages, and platforms.
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Based on our record, NumPy seems to be a lot more popular than Apache ActiveMQ. While we know about 119 links to NumPy, 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 / 3 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 / 3 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 / about 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
The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 4 months ago
This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / 8 months ago
The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / 9 months ago
It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”. - Source: dev.to / 9 months ago
The OpenCV image is a regular NumPy array. You can see it shape:. - Source: dev.to / 9 months ago
RabbitMQ - RabbitMQ is an open source message broker software.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
IBM MQ - IBM MQ is messaging middleware that simplifies and accelerates the integration of diverse applications and data across multiple platforms.
OpenCV - OpenCV is the world's biggest computer vision library
Apache Kafka - Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.