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Kafka vs Kinesis

By Priya PedamkarPriya Pedamkar

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Difference Between Kafka and Kinesis

Apache Kafka is an open-source stream-processing software developed by LinkedIn (and later donated to Apache) to effectively manage their growing data and switch to real-time processing from batch-processing. It is written in Scala and Java and based on the publish-subscribe model of messaging. Kinesis is a managed platform developed by Amazon to collect and process large streams of data records in real-time. It is modeled after Apache Kafka. It is known to be incredibly fast, reliable, and easy to operate. Kafka Vs Kinesis are both effectively amazing.

Head to Head Comparison Between Kafka and Kinesis(Infographics)

Below are Top 5 Differences between Kafka vs Kinesis:

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Key Differences Between Kafka and Kinesis

The key differences between Kafka and Kinesis are mentioned below:

  • Kafka is an open-source distributed messaging solution whereas Kinesis is a managed platform offered by Amazon. In Kafka, you are responsible for installing and managing clusters, and you also are responsible for ensuring high availability, durability, and failure recovery. If you are using Kinesis, you don’t have to be concerned with hosting the software and the resources. You can learn Kafka easily by installing it in your local system whereas it’s not the same for Kinesis.
  • Pricing in Kinesis depends on the number of shards you are using. You will also have to pay extra bucks if you are planning to keep the messages for an extended duration. In the case of Kafka, the cost primarily depends on the number of Brokers you are using. Kafka additionally requires a DevOps team for maintenance which is costly at times. But with Kafka, you can keep your messages for longer duration without paying extra money as long as you don’t run out of storage space.
  • Although both Kafka and Kinesis comprise of Producers, Kafka producers write messages to a topic whereas Kinesis Producers write data to KDS. Kinesis also imposes certain restrictions on message size and consumption rate of messages. The maximum message size in Kinesis is 1 MB whereas, Kafka messages can be bigger. In Kinesis, you can consume 5 times per second and up to 2 MB per shard, which in turn can write only 1000 records per second. Kafka doesn’t impose any implicit restrictions, so rates are determined by the underlying hardware.
  • On the Security front, Kafka offers many Client-side security features like data encryption, Client Authentication, and Client Authorization whereas Kinesis provides server-side encryption with AWS KMS master keys to encrypt data stored in your data stream. Server-Side encryption has the following advantages:
  1. It is hard to enforce client-side encryption.
  2. Server-Side encryption provides a second layer of security on top of client-side encryption.

Kafka vs Kinesis Comparison Table

Let us discuss the top 5 difference between Kafka vs Kinesis:

Basis of Comparison   Kafka Kinesis
Meaning 1. It is an open-source stream-processing software platform.
2. It can be installed and run in your local machine.
3. You can store data for as many days as required.		 1. It a paid platform to collect and process large streams of data.
2. It is a cloud service and cannot be run locally.
3. Kinesis stores data for 24 hours by default which can be increased to up to 7 days by changing some configuration.
Cost 1. It (Kafka application) is available for free.
2. The initial setup cost is huge.
3. The cost is proportional to the number of brokers.
4. Running a Kafka cluster is more of a fixed cost. You can definitely add more brokers if needed but you aren’t going to shut down a broker because you’re at a low point.		 1. You have to opt for AWS (which is a paid service) in order to use Kinesis.
2. The setup cost is low.
3. Cost is proportional to the number of shards you are using.
4. You will change the number of shards to optimize costs based on demand. For example, if you had a low point during the day, you could go down to lesser shards and save money.
Architecture 1. The key components of the Kafka Ecosystem include Producers, Consumers, Topics.
2. Producers push messages into topics which in turn consist of partitions.
3. A topic is a partitioned log of records with each partition being ordered and immutable.		 1. The key components of AWS kinesis are Producers, Consumers, and Kinesis Data Streams(KDS).
2. Producers push messages into KDS which in turn consists of shards.
3. Each shard has a sequence of data records. Data records are composed of a sequence number, a partition key, and a data blob (up to 1 MB), which is an immutable sequence of bytes.
Operations 1. You have to manage and maintain your Kafka cluster yourself and this requires a lot of human resources.
2. You have to take care of replication and scaling.
3. If the cluster has enough resources, scaling up simply means adding more partitions. If your Kafka cluster doesn’t have enough resources, you will need to install and configure another broker, then add more partitions.		 1. As Kinesis is a managed platform, the efforts on maintenance are way lesser.
2. You don’t need to bother much about replication and scaling.
3. In Kinesis, you just need to call an API to increase the number of shards.
Security 1. Kafka supports client-side security features like:
Ø Encrypt data-in-transit between your applications and Kafka brokers.
Ø Client authentication.
Ø Client authorization.		 1. For data security, you can use server-side encryption with AWS KMS master keys to encrypt data stored in your data stream. AWS KMS allows you to use AWS generated KMS master keys for encryption, or if you prefer you can bring your own master key into AWS KMS. Lastly, you can use your own encryption libraries to encrypt data on the client-side before putting the data into Kinesis.

Conclusion

Both Kafka and Kinesis provide a good platform for real-time data processing, it depends on the organization which one it prefers. If an organization doesn’t have enough Apache Kafka experts/ Human resources then it should consider Kinesis. But if wishes to keep messages within its clusters and for a longer duration, it will go with Kafka.

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