In this blog, “Apache Kafka vs Storm: Difference Between Storm and Kafka” we will see the complete comparison for both Kafka and Storm. So, let’s start with the brief introduction of Kafka and Storm to understand the comparison well.
What is Kafka
In order to enable communication between Kafka Producers and Kafka Consumers using message-based topics, we use Apache Kafka. It is very fast, scalable and fault-tolerant, publish-subscribe messaging system.
Kafka plays the role of a platform for high-end new generation distributed applications. Moreover, it permits a huge number of permanent or ad-hoc consumers. As a benefit, Kafka is highly resilient to node failures and also offers automatic recovery.
Hence we can say Kafka is the best choice for communication and integration between components of large-scale data system because of this special feature.
What is Storm?
An open source, distributed, reliable, and fault-tolerant system, is Apache Storm. It has several uses, for example, the Extract Transformation Load (ETL) paradigm, real-time analytics, online machine learning, and continuous computation.
It has various components that work together for the purpose of streaming as well as data processing such as Spout and Bolt. On defining both:
Spout: A source of the stream is what we call Spout.
Bolt: Whereas, Bolt is a component to which, spout passes the data.
Topology: Storm topology is the combination of Spout and Bolt. It is the same as
the Map and Reduces in Hadoop.
Stream: Stream can be considered as Data Pipeline it is the actual data that we received from a data source. Now, let’s start the feature wise Comparison of Kafka Vs Storm.
Apache Kafka vs Storm
Here are some Key Differences Between Apache Kafka vs Storm:
| Comparison | Apache Kafka | Storm |
| Data Security | Kafka does not guarantee data loss, or we can say it have the very low guarantee. For Example, for 7 Million message transactions per day, Netflix achieved 0.01% of data loss. | On comparison with Kafka, Storm guarantees full data security. |
| Data Storage | Apache Kafka store its data on the local filesystem, such as EXT4 and XFS. | Storm is just a data processing framework. That says it doesn’t store data it just transfers it from input to Output stream. |
| Real-time messaging system | Before processing only, Kafka used to store incoming messages. | Storm works on a Real-time messaging system. |
| Processing/Transforming | We use Apache Kafka for processing the real-time data. | We use Storm for transforming the data. |
| Data Source | Kafka pulls the data from the actual source of data. | Storm gets the data from Kafka itself regarding further processes. |
| Basic Tasks | While it comes to transferring real-time application data from the source application to another, we use Kafka application. | we use Storm for aggregation as well as computation purpose. |
| Zookeeper Dependency | While setting up the Kafka, it’s mandatory to have Apache Zookeeper. | we don’t need Zookeeper to make Storm work. |
| Fault-Tolerant | Due to Zookeeper, Kafka is fault tolerant. | The storm is capable of auto-restart its daemons itself. |
| Inventor | Kafka is invented by LinkedIn. | Twitter invented Apache Storm. |
| Language Support | Kafka can work with all languages but while it comes to work best, Kafka works best with Java language only. | Strom supports all the languages. |
| Latency | Kafka’s Latency depends upon Data Source, which is generally less than 1-2 seconds. | While it comes to latency, it is Millisecond latency. |
| Stream Processing | Kafka performs Small-Batch Processing. | Storm Performs Micro-Batch Processing. |
Apache Kafka vs Storm both are independent and have a different purpose in Hadoop cluster environment. Apache Kafka vs Storm both are independent of each other however it is recommended to use Storm with Kafka as Kafka can replicate the data to storm in case of packet drop also it authenticate before sending it to Storm.
Kafka’s role is to work as middleware it takes data from various sources and then Storms processes the messages quickly. Counting and segregating of online votes is the real-time example for Apache Storm.
Apache Kafka vs Storm both are having great capability in the real-time streaming of data and very capable systems for performing real-time analytics.
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