Apache Kafka and Apache Flume are two of the most widely used technologies for data ingestion and centralized data storage respectively
In this article, we will be looking at the difference between Apache Kafka and Apache Flume.
Apache Kafka:
- Apache Kafka is a distributed data system.
- Kafka is optimized for ingesting and processing streaming data in real time.
- Kafka basically works as a pull model.
- Kafka is easy to scale.
- Kafka is a fault-tolerant, efficient, and scalable messaging system.
- Kafka supports automatic recovery if resilient to node failure.
- Kafka runs as a cluster that handles the incoming high-volume data streams in real time.
- Kafka will treat each topic partition as an ordered set of messages.
Apache Flume:
- Apache Flume is an available, reliable, and distributed system.
- Flume is efficiently collecting, aggregating, and moving large amounts of log data from many different sources to a centralized data store.
- Flume basically works as a push model
- Flume is not scalable in comparison with Kafka.
- Flume is specially designed for Hadoop.
- You will lose events in the channel in case of flume-agent failure.
- Flume is a tool to collect log data from distributed web servers.
- Flume can take in streaming data from multiple sources for storage and analysis which use in Hadoop.
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