Apache Kafka vs Amazon Kinesis: Comparing The Two Popular Stream Processors

Apache Kafka and Amazon Kinesis are the two most popular and widely used messaging queue systems. Many big organizations that are dealing with stream processing often find themselves in a confusing situation and couldn’t decide whether to use open-source Apache Kafka services or Amazon-managed Kinesis services.

In this blog, we will discuss some points of difference between Apache Kafka vs Amazon Kinesis so that you can make a more informed decision and choose the right platform for your organization.

Apache Kafka

Apache Kafka was originally started as a publishing and subscribe messaging system but later evolved as a fully developed, scalable, fault-tolerant, and highly performing streaming platform.

Kafka runs on a cluster in a distributed environment. The Kafka cluster is made of several Kafka brokers. There is a topic that is designed to store data streams. Every topic is divided into multiple partitions. Applications send data streams to a partition through producers. These data streams are later consumed and processed by consumers. 

Amazon Kinesis

Amazon Kinesis is also a publish and subscribe messaging service, but the difference is that it is offered as a managed service in the AWS cloud and you can not run it on-premise.

The producer in the Kinesis pushes data into Kinesis Streams. The consumer processes the data in real-time. Just like the partitions in Kafka, Kinesis breaks the streams across shards. 

Apache Kafka vs Amazon Kinesis: The critical differences

• Architecture: Apache Kafka vs Amazon Kinesis

Apache Kafka- The architecture of Apache Kafka has producers and consumers which plays a very important role. Producers are the client applications that write events to Kafka. Consumers read and process these events. Kafka has decoupled producers and consumers to achieve scalability. An event is organized and stored in the topic. These topics are partitioned into multiple buckets that are located on various Kafka brokers. Whenever a new event is published on a topic, they are attached to one of the topic’s partitions. Events in any topic can be read often.

Amazon Kinesis- 

The architecture of Amazon Kinesis is a collection of shards. A shard is a unique collection of the records of data in a stream and can support up to 5 transactions per second.

Kinesis uses a key called partition that is associated with each data record to determine the given data record belongs to which shard. Whenever any application injects data into a stream, it should specify a partition key. The capacity of the stream depends on the number of shards. 

• SDK support: Apache Kafka vs Amazon Kinesis

Apache Kafka- 

Kafka is a stream processing Java API that is offered by open-source Apache Kafka. Any Java or Scala application that utilizes the Kafka stream library is considered a Kafka stream application. Suppose if any application is written in Scala, developers can use Kafka stream for Scala library that removes interoperability of Java/Scala which generally happens when we work directly with Java DSL.

Amazon Kinesis-

Amazon Kinesis SDKs for Go, Java, JavaScript, .NET, PHP, Python, etc supports data streams. KCL provides a programming model for processing the data. This model is easy to use and users can start quickly.

• Retention: Apache Kafka vs Amazon Kinesis

Apache Kafka- 

The retention period is the period various data records are accessible after they are added to the stream. Apache Kafka has a default retention period of seven days. Users can also change this period using various configurations.

Amazon Kinesis-

The retention period of Amazon Kinesis is set to the default of 24 hours after the creation. Users can increase the retention period up to 365 days. They can also cut down the retention period as they like to a minimum of 24 hours.

• Monitoring: Apache Kafka vs Amazon Kinesis

Apache Kafka-

Kafka stream offers various metrics through JMX (Java Management Extensions). Some built-in metrics are-

• Client metrics- Versions of the Kafka stream client, topology, and the state of the client.
• Thread metrics- Includes execution time, the time spent by the thread on operations of active tasks.
• Task metrics- Average number of respective tasks per second, the lateness of task, measure end-to-end latency.

Amazon Kinesis-

Users can monitor data streams by using these features-

• Cloud watch metrics
• Kinesis agent
• Kinesis client library
• Kinesis producer library

Developers can add additional metrics

• Pricing: Apache Kafka vs Amazon Kinesis

Apache Kafka–

Apache Kafka is open-source and thus has no initial costs. 

Amazon Kinesis-

Amazon Kinesis has provision-based pricing. The pricing is calculated based on the shard hours, payloads units, and data retention.

Conclusion

As we have discussed earlier the difference between Apache Kafka vs Amazon Kinesis, we can say that both are equally popular and offer various extensive features. Apache Kafka is surely a better choice as it offers more flexibility with configurations. Ksolves is a leading Apache Kafka development company in India and the USA offering Apache Kafka consulting services. We have one of the best teams of Apache Kafka developers who are qualified professionals handling challenging situations. If you want to utilize the power of Apache Kafka, Ksolves is the right choice for you. Write your queries in the comments section below. 

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