Apache Storm vs Kafka

Difference Between Apache Storm and Kafka

Apache Kafka use to handle a big amount of data in the fraction of seconds. It is a distributed message broker which relies on topics and partitions. Apache Storm is a fault-tolerant, distributed framework for real-time computation and processing data streams. It takes the data from various data sources such as HBase, Kafka, Cassandra, and many other applications and processes the data in real-time. It has been written in Clojure and Java.

Let us study more about Apache Storm vs Apache Kafka in detail:

Figure 1, Basic Stream Processing Diagram of Apache Storm

In Figure1, Basic stream processing is carried out. Spout and Bolt are two main components of Apache Storm and both are the part of Storm Topology which takes the data stream from data sources to process it.

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.

Spout: Spout receive data from different-different data sources such as APIs. It continuously receives data from data sources and sends it to Bolt for processing.

Bolt: It is logical processing units take data from Spout and perform logical operations such as aggregation, filtering, joining & interacting with data sources and databases.

Apache Kafka provides real-time data streaming. It takes the data from different websites such as Facebook, Twitter, and APIs and passes the data to any different processing application (Apache Storm) in a Hadoop environment.

Figure 2, Architecture and components of Apache Kafka.

Kafka stores messages/data which it received from different data sources call “Producer“. Once it receives the data it partitioned the messages through “Partition” within different “Topic“.

Kafka Cluster is a combination of Topics and Partitions. The Partitions indexes and stores the messages. The consumer takes the messages from partitions and queries the messages. Apache Kafka can be used along with Apache HBase, Apache Spark, and Apache Storm.

The following are the APIs that handle all the Messaging (Publishing and Subscribing) data within Kafka Cluster.

1) Producer API: It provides permission to the application to publish the stream of records.

2) Consumer API: This API is being used to subscribe to the topics.

3) Stream API: This Stream provides the result after converting the input stream into the output stream.

4) Connector API: This links the topics with existing applications.

The main use of Apache Kafka is for Website Activity Tracking, Metrics, Log Aggregation, Event Sourcing, and other live data stream capturing. It is good for streaming that reliably gets data between applications or systems

Head to Head Comparison Between Apache Storm and Kafka (Infographics)

Below is the Top 9 Differences between Apache Storm and Kafka:

Key Differences Between Apache Storm and Kafka

Following is the key difference between Apache Storm and Kafka:

1) Apache Storm ensure full data security while in Kafka data loss is not guaranteed but it’s very low like Netflix achieved 0.01% of data loss for 7 Million message transactions per day.

2) Kafka can store its data on local filesystem while Apache Storm is just a data processing framework.

3) Storm works on a Real-time messaging system while Kafka used to store incoming message before processing.

4) Apache Kafka is used for processing the real-time data while Storm is being used for transforming the data.

5) Kafka gets its data from the actual source of data while Storm pulls the data from Kafka itself for further processes.

6) Kafka is an application to transfer real-time application data from source application to another while Storm is an aggregation & computation unit.

7) Kafka is a real-time streaming unit while Storm works on the stream pulled from Kafka.

8) It’s mandatory to have Apache Zookeeper while setting up the Kafka other side Storm is not Zookeeper dependent.

9) Kafka works as a water pipeline which stores and forward the data while Storm takes the data from such pipelines and process it further.

10) Kafka is a great source of data for Storm while Storm can be used to process data stored in Kafka.

11) Apache Storm has inbuilt feature to auto-restart its daemons while Kafka is fault-tolerant due to Zookeeper.

Apache Storm and Kafka Comparison Table

Below is the comparison table between Apache Storm and Kafka.

Comparison Points	Storm	Kafka
Inventor	Twitter	LinkedIn
Type	Real Time Message Processing	Distributed Messaging System
Data Source	Kafka & any database system	FB, Twitter etc…
Primary Use	Stream Processing	Message Broker
Data Storage	Doesn’t store its data. Data gets transfer from input stream to output stream	File system such as EXT4 or XFS
Stream Processing	Micro-Batch Processing	Small-Batch Processing
Dependency	Not Dependent on any external application	Zookeeper Dependent
Latency	Milli-Second latency	Depends upon Data Source generally less than 1-2 seconds.
Language Support	It supports all the languages	Kafka works with all but works best with Java language only

Conclusion

Apache Storm vs Kafka both are independent and have a different purpose in Hadoop cluster environment.

Apache Storm vs Kafka 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 Storm vs Kafka both are having great capability in the real-time streaming of data and very capable systems for performing real-time analytics.

Recommended Articles

This has been a guide to Apache Storm vs Kafka. Here we have discussed Apache Storm vs Kafka 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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