Difference Between Apache Kafka and Flume
Apache Kafka is an open source system for processing ingests data in real-time. Kafka is the durable, scalable and fault-tolerant public-subscribe messaging system. The publish-subscribe architecture was initially developed by LinkedIn to overcome the limitations in batch processing of large data and to resolve issues on data loss. The architecture in Kafka will disassociate the information provider from the consumer of information. Hence, the sending application and the receiving application will not know anything about each other for that data sent and received.
Apache Kafka will process incoming data streams irrespective of their source and its destination. It is a distributed streaming platform with capabilities similar to an enterprise messaging system but has unique capabilities with high levels of sophistication. With Kafka, users can publish and subscribe to information as and when they occur. It allows users to store data streams in a fault-tolerant manner. Irrespective of the application or use case, Kafka easily factors massive data streams for analysis in enterprise Apache Hadoop. Kafka also can render streaming data through a combination of Apache HBase, Apache Storm, and Apache Spark systems and can be used in a variety of application domains.
In simplistic terms, Kafka’s publish-subscribe system is made up of publishers, Kafka cluster, and consumers/subscribers. Data published by the publisher are stored as logs. Subscribers can also act as publishers and vice-versa. A subscriber requests for a subscription and Kafka forwards the data to the requested subscriber. Typically, there can be numerous publishers and subscribers on different topics on a Kafka cluster. Likewise, an application can act as both, a publisher and subscriber. A message published for a topic can have multiple interested subscribers; the system processes data for every interested subscriber. Some of the use cases where Kafka is widely used are:
- Track activities on a website
- Stream processing
- Collecting and monitoring metrics
- Log Aggregation
Apache Flume is a tool which is used to collect, aggregate and transfer data streams from different sources to a centralized data store such as HDFS (Hadoop Distributed File System). Flume is highly reliable, configurable and manageable distributed data collection service which is designed to gather streaming data from different web servers to HDFS. It is also an open source data collection service.
Apache Flume is based on streaming data flows and has a flexible architecture. Flume offers highly fault-tolerant, robust and reliable mechanism for fail-over and recovery with the capability to collect data in both batch and in stream modes. Flume’s capabilities are leveraged by enterprises to manage high volume streams of data to land in HDFS. For instance, data streams include application logs, sensors and machine data and social media, and so on. These data, when landed in Hadoop, can be analyzed by running interactive queries in Apache Hive or serve as real-time data for business dashboards in Apache HBase. Some of the features include,
- Gather data from multiple sources, and efficiently ingest into HDFS
- A variety of source and destination types are supported
- Flume can be easily customized, reliable, scalable and fault-tolerant
- Can store data in any centralized store (eg., HDFS, HBase)
Head to Head Comparison Between Apache Kafka and Flume (Infographics)
Below is the Top 5 Comparision Between Apache Kafka and Flume:
Key Differences Between Apache Kafka and Flume
The differences between Apache Kafka and Flume are explored here,
- Both, Apache Kafka and Flume systems provide reliable, scalable and high-performance for handling large volumes of data with ease. However, Kafka is a more general purpose system where multiple publishers and subscribers can share multiple topics. Contrarily, Flume is a special purpose tool for sending data into HDFS.
- Kafka can support data streams for multiple applications, whereas Flume is specific for Hadoop and big data analysis.
- Kafka can process and monitor data in distributed systems whereas Flume gathers data from distributed systems to land data on a centralized data store.
- When configured correctly, both Apache Kafka and Flume are highly reliable with zero data loss guarantees. Kafka replicates data in the cluster, whereas Flume does not replicate events. Hence, when a Flume agent crashes, access to those events in the channel is lost till the disk is recovered, on the other hand, Kafka makes data available even in case of single point failure.
- Kafka supports large sets of publishers and subscribers and multiple applications. On the other hand, Flume supports a large set of source and destination types to land data on Hadoop.
Apache Kafka vs Flume Comparison Table
The comparison table between Apache Kafka and Flum is mentioned below.
|Basis for Comparison||Apache Kafka||Flume|
|Basis of Formation||
In summary, Apache Kafka vs Flume offer reliable, distributed and fault-tolerant systems for aggregating and collecting large volumes of data from multiple streams and big data applications. Both Apache Kafka and Flume systems can be scaled and configured to suit different computing needs. Kafka’s architecture provides fault-tolerance, but Flume can be tuned to ensure fail-safe operations. Users planning to implement these systems must first understand the use case and implement appropriately to ensure high performance and realize full benefits.
This has been a guide to Apache Kafka vs Flume. Here we have discussed Apache Kafka vs Flume head to head comparison, key difference along with infographics and comparison table. You may also look at the following articles to learn more –
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