ActiveMQ vs Hadoop: What are the differences?
Developers describe ActiveMQ as "A message broker written in Java together with a full JMS client". Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License. On the other hand, Hadoop is detailed as "Open-source software for reliable, scalable, distributed computing". The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
ActiveMQ and Hadoop are primarily classified as "Message Queue" and "Databases" tools respectively.
"Open source" is the top reason why over 9 developers like ActiveMQ, while over 34 developers mention "Great ecosystem" as the leading cause for choosing Hadoop.
ActiveMQ and Hadoop are both open source tools. It seems that Hadoop with 9.27K GitHub stars and 5.78K forks on GitHub has more adoption than ActiveMQ with 1.51K GitHub stars and 1.05K GitHub forks.
According to the StackShare community, Hadoop has a broader approval, being mentioned in 237 company stacks & 127 developers stacks; compared to ActiveMQ, which is listed in 33 company stacks and 17 developer stacks.
What is ActiveMQ?
What is Hadoop?
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Since the beginning, Cal Henderson has been the CTO of Slack. Earlier this year, he commented on a Quora question summarizing their current stack.Apps
- Desktop: And Electron to ship it as a desktop application.
- Android: a mix of Java and Kotlin.
- iOS: written in a mix of Objective C and Swift.
- The core application and the API written in PHP/Hack that runs on HHVM.
- The data is stored in MySQL using Vitess.
- Caching is done using Memcached and MCRouter.
- The search service takes help from SolrCloud, with various Java services.
- The messaging system uses WebSockets with many services in Java and Go.
- Load balancing is done using HAproxy with Consul for configuration.
- Most services talk to each other over gRPC,
- Some Thrift and JSON-over-HTTP
- Voice and video calling service was built in Elixir.
- Built using open source tools including Presto, Spark, Airflow, Hadoop and Kafka.
The MapReduce workflow starts to process experiment data nightly when data of the previous day is copied over from Kafka. At this time, all the raw log requests are transformed into meaningful experiment results and in-depth analysis. To populate experiment data for the dashboard, we have around 50 jobs running to do all the calculations and transforms of data.
in 2009 we open sourced mrjob, which allows any engineer to write a MapReduce job without contending for resources. We’re only limited by the amount of machines in an Amazon data center (which is an issue we’ve rarely encountered).
The massive volume of discovery data that powers Pinterest and enables people to save Pins, create boards and follow other users, is generated through daily Hadoop jobs...
Remote broker and local client for incoming data feeds. Local broker for republishing data feeds to other systems.
Importing/Exporting data, interpreting results. Possible integration with SAS