Kinesis vs Kafka

What is Kinesis?

Kinesis is a cloud-based real-time streaming platform fully managed by Amazon Web Services (AWS). Kinesis can be used to collect and process a large amount of data in real-time streaming. Kinesis stores data either in the form of binary or JSON. The data records in Kinesis are immutable.

AWS Kinesis has various applications like doll collecting analog sis data from IoT devices, mobile devices, web applications,ons, etc. It provides real-time capabilities like real-time analytics and machine learning. We can use Kinesis Data Streams to analyze data from various data streams. Kinesis integrates well with Amazon-based services. Kinesis helps you to write to 3 different synchronous machines. This is done to ensure fault tolerance but slows down the performance of Kinesis. Amazon DynamoDB helps us checkpoint the last read message. This keeps track of the last message/ record read from the Shard. This helps us to continue reading messages even after a failure.

Amazon Kinesis offers you pay-as-you-go pricing. The prices depend on the following two things: The number of shards you use for obtaining the required throughput

1. The data size which the producer is transmitting to the KDS (Kinesis Data Streams)
2. The cost for Amazon Kinesis decreases with time.

Python, Java, Scala, etc, are used to write Kinesis. Kinesis supports Java, .NET, Android,d, and Go. Let us see how Kinesis works:

1. Data producers: Data producers generate records in Kinesis Data Streams.
2. Data consumers: Data consumers read and process data from Kinesis Data Streams.
3. Integration: Kinesis can integrate with various Amazon products. Thus using Kinesis makes it easy for those already using Amazon products.
4. Shards: The data stream consists of one or more Shards.
5. Processing: Amazon Kinesis offers real-time processing like data analytics and machine learning.

Netflix, the streaming media service where people watch TV shows and movies uses AWS Kinesis for processing and analyzing the data in real-time to get feedback on their data. They use this feedback to improve their services.

What is Apache Kafka?

Apache Kafka is a distributed Open Source platform for real-time streaming data pipelines. Kafka is a pub-sub handling model designed to handle data streaming of a large amount of data. It was initially made by LinkedIn but now Apache owns Kafka.

Kafka is known for its high performance and fault tolerance. Kafka ensures fault tolerance by replicating data across multiple servers. Kafka is highly flexible. Kafka stores data in kafka partition. The data storage type in Kafka is bytes. The data records in Kafka are also immutable. The maximum size of a single Kafka message can be 2MB.

Kafka captures data from producers which writes data into Kafka topics. Kafka consumer reads data from Kafka topics. Kafka is implemented in Java and Scala but allows Python, Java, Scala, etc can be used to communicate with Kafka clusters and topics. Apache Zookeeper acts as a configuration store for Kafka.

Kafka connects well with several third-party configurations like Apache Hadoop, Apache Spark, and Elasticsearch.

The main components of Kafka are:

1. Producers: Kafka producers write messages to Kafka topics. Each message is in the form of a key, value, and an optional timestamp.
2. Consumers: Kafka consumers read messages from Kafka topics.
3. Brokers: Brokers manage data replication and storage.
4. Topic: Here the messages are written by Kafka producers and read by Kafka consumers.
5. Partitions: The partition consists of a sequence of messages stored by the arrival time on Brokers.

Let us see the basic data flow in Kafka:

1. Producers write messages to Kafka topics.
2. Consumers read messages from Kafka topics.
3. Brokers receive messages from Kafka producers and store them in Kafka partitions.
4. The messages stored also have an offset with them so that we can mark the last message read from the Kafka topic. Offset also helps us to uniquely identify each message. By using offset, we can keep track of the last message/ record read from the partition and continue reading messages even after a failure.

Key Points to Choose Amazon Kinesis vs Kafka

Apache Kafka and Amazon Kinesis both of them have pros and cons. One can select the software depending on the need to use it. The following things can be considered while selecting the software:

1. Performance and fault tolerance:
   Kinesis writes synchronously into 3 different machines. Kafka can be configured and thus can be used to write in as many machines for fault tolerance. It allows one to write and read data from only one machine for faster performance. Thus Kafka can be configured to provide more fault tolerance and higher performance.
2. Cost:
   Apache Kafka is open source and thus is free to use. Amazon Kinesis provides a pay-as-you-go model. However, the configuration for Kafka is much more complex and may lead to higher costs. Amazon Kinesis however handles complex structures like set up etc.
3. Scalability:
   Apache Kafka and kinesis both are highly scalable. Amazon Kinesis can automatically scale up and down based on the requirement. Kafka requires manual help for scaling up or down.
4. Flexibility:
   Kafka provides much more flexibility. Kinesis can be deployed on the AWS cloud whereas Kafka can be deployed on a variety of platforms including the Cloud.
5. Community:
   Kafka is open source and thus has a large community. Amazon Kinesis has a smaller amount of community.
6. Data processing:
   Kafka can process a large amount of data white data streaming. But Kinesis provides real-time streaming along with real-time analytics and machine learning.
7. Throughput:
   Kinesis can reach a throughput of 1000 messages/second. Kafka with configuration can reach a throughput of 30k messages/second. Thus we can reach a much higher throughput by using Kafka than Amazon Kinesis.
8. Easy to use:
   Kafka requires a lot of human hours to work with its complex structures. On the other hand, Amazon Kinesis is backed by Amazon and thus is easy to use and less time-consuming.
9. Security:
   Kafka requires a large number of engineers to write code and thus may lead to few errors in the code. On the other hand, Amazon Kinesis provides a great amount of security.

Before we see the difference between them, let us see the key points which we have to keep in mind, while choosing Kafka vs AWS Kinesis. Thus we conclude that we may have to choose aAWSKinesis vs Kafka based on our requirements like cost, security, flexibility, and throughput. Thus if you know how to maintain Apache Kafka and Apache Zookeeper and want to process more than 1k messages/second, then Apache Kafka becomes the best option for us. While if we don’t knowledge of maintaining Apache Zookeeper and want easier setup, or want to do data analytics on the data, aws kinesis becomes a better option for us.

Let us now jump on to our main topic AWS Kinesis vs Kafka.

Kinesis vs Kafka

It's time to compare AWS Kinesis vs Kafka. Let us compare both of them using a table.