There are various options to use as a messaging system with Big Data. These kinds of systems are widely known as the 'Distributed Commit Log Technologies'. Apart from delivery guarantees and flexibility, theses messaging systems are more inclined towards Throughput and Scalability. Some of the most used Big Data messaging systems are : Apache Kafka Microsoft Event Hubs Amazon Kinesis Google Pub/Sub Rabbit MQ 'Apache Kafka' is a messaging system that is distributed in nature. It allows having publish-subscribe kind of messages in a data pipeline. It is a highly scalable and fast messaging system. It centralizes communication between large data systems. It is mostly used as a 'Central Messaging System'.  Kafka is fault-tolerant and so is more reliable. It gives high performance. Originally it was developed at 'LinkedIn'. 'Microsoft Event Hubs' are event investors. They receive and process millions of events/second. Producers of the events send them to an event hub through AMQP/HTTPS. 'Amazon Kinesis' is a cloud-based messaging service. It's processing in real-time. Google Pub/Sub is also a cloud-based messaging service. Here the 'Consumers' subscribe to a topic and the 'Publishers' send the messages to a topic. Rabbit MQ is a 'Message Queuing System'. It serves as a middleware between the 'Producers' and the 'Consumers.'