Kafka vs Amazon Kinesis: Choosing the Right Streaming Platform

Kafka and Kinesis are two popular streaming data platforms that enable real-time data processing. Kafka is an open-source distributed streaming platform that is used for building real-time data pipelines and streaming applications. It is designed to handle high-volume data streams and provides features such as fault-tolerance and scalability. Kinesis, on the other hand, is a managed service provided by Amazon Web Services (AWS) that allows users to build real-time applications that can process streaming data at scale.

While both Kafka and Kinesis are used for real-time data processing, they have some key differences. Kafka is a more mature platform that has been around for over a decade and has a strong community of users and contributors. It is known for its high throughput and low latency, making it a popular choice for processing large volumes of data in real-time. Kinesis, on the other hand, is a newer platform that is fully managed by AWS, which means users don’t have to worry about managing infrastructure or scaling their applications.

Despite their differences, both Kafka and Kinesis have their own strengths and weaknesses, and choosing the right platform depends on the specific needs of each use case. In the following article, we will explore the differences between Kafka and Kinesis in more detail and provide guidance on how to choose the right platform for your streaming data needs.

*** Updated Spring 2020 ***

Since this original post, AWS has released MSK. I think this tells us everything we need to know about Kafka vs Kinesis. Also, since the original post, Kinesis has been separated into multiple “services” such as Kinesis Video Streams, Data Streams, Data Firehose, and Data Analytics. I’ll make updates to the content below, but let me know if any questions or concerns

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Overview

Like many of the offerings from Amazon Web Services, Amazon Kinesis software is modeled after an existing Open Source system. In this case, Kinesis is appears to be modeled after a combination of pub/sub solutions like RabbitMQ and ActiveMQ with regards to the maximum retention period of 7 days and Kafka in other ways such as sharding.

Kinesis is known to be reliable, and easy to operate. If you don’t have need for scale, strict ordering, hybrid cloud architectures, exactly-once semantics, it can be a perfectly fine choice. If you don’t have a need for certain pre-built connectors compared to Kafka Connect or stream processing with Kafka Streams / KSQL, it can also be a perfectly fine choice.

Similar to Kafka, there are plenty of language-specific clients available for working with Kinesis including Java, Scala, Ruby, Javascript (Node), etc.

Amazon Kinesis has a built-in cross replication while Kafka requires configuration to be performed on your own. Cross-replication is the idea of syncing data across logical or physical data centers. Cross-replication is not mandatory, and you should consider doing so only if you need it.

What is Kafka?

Kafka’s Architecture

Kafka is a distributed streaming platform developed by Apache. It is designed to handle large amounts of data in real-time, allowing users to publish, subscribe, and process streams of records. Kafka’s architecture is based on the publish-subscribe model, where producers write data to topics and consumers read from those topics.

Kafka’s Key Features

Kafka’s key features include:

High throughput: Kafka is designed to handle high volumes of data, making it suitable for use cases that require real-time processing of large amounts of data.
Fault-tolerant: Kafka’s architecture is designed to be fault-tolerant, ensuring that data is not lost in the event of a node failure.
Scalable: Kafka can be scaled horizontally by adding additional brokers to the cluster, allowing it to handle increasing volumes of data.

Low latency: Kafka’s architecture is optimized for low latency, ensuring that data is processed quickly and efficiently.
Durability: Kafka stores data on disk, ensuring that data is not lost in the event of a node failure.

What is Kinesis?

Kinesis is a fully managed real-time data streaming service provided by Amazon Web Services (AWS). It is designed to allow developers to ingest, process, and analyze streaming data in real-time. Kinesis is a scalable service that can handle large volumes of data from various sources, such as social media feeds, logs, and IoT devices.

Kinesis’s Architecture

Kinesis has a distributed architecture that consists of three main components: producers, streams, and consumers. Producers are responsible for ingesting data into Kinesis streams, which are scalable and durable storage units that store the data for a configurable period of time. Consumers can then read the data from the streams and process it in real-time.

Kinesis also provides a set of APIs that allow developers to interact with the service programmatically. These APIs can be used to create and manage Kinesis streams, as well as to put and get data from the streams.

Kinesis’s Key Features

Kinesis has several key features that make it a popular choice for real-time data streaming applications. Some of these features include:

Scalability: Kinesis is designed to handle large volumes of data, and can scale up or down based on the amount of data being ingested.
Durability: Kinesis streams are highly durable and can store data for up to 7 days by default. This ensures that data is not lost in case of failures or outages.

Real-time processing: Kinesis allows developers to process data in real-time, which can be useful for applications that require immediate insights from streaming data.
Integration with other AWS services: Kinesis can be integrated with other AWS services, such as Lambda and S3, to enable a wide range of use cases.

Kafka vs Kinesis: Performance

When it comes to performance, both Kafka and Kinesis are capable of handling high throughput and low latency data streaming.

Kafka Performance

Kafka is known for its high throughput capabilities and low latency. It can handle millions of messages per second and is optimized for both read and write operations. Kafka achieves this by utilizing a distributed architecture that allows for horizontal scaling. It can easily scale up or down based on the demand and can handle large amounts of data without any performance degradation.

Kafka also provides features like data partitioning, which allows for parallel processing of data, and replication, which ensures data availability and fault tolerance. These features make Kafka an ideal choice for use cases that require high throughput and low latency.

Kinesis Performance

Kinesis is also designed to handle high throughput and low latency data streaming. It can handle millions of events per second and can scale up or down based on demand. Kinesis also provides features like data partitioning and replication, which ensure high availability and fault tolerance.

One advantage of Kinesis is its ability to integrate with other AWS services. This allows for easy integration with other services like Lambda, which can be used to process data in real-time. Kinesis also provides features like Kinesis Firehose, which allows for easy data delivery to other AWS services like S3 and Redshift.

Kafka vs Kinesis Performance Comparison

When it comes to performance, both Kafka and Kinesis are capable of handling high throughput and low latency data streaming. However, Kafka is known for its high throughput capabilities and low latency, making it an ideal choice for use cases that require high performance. On the other hand, Kinesis provides easy integration with other AWS services, making it an ideal choice for use cases that require easy integration with other services.

The key performance difference can be found in horizontal scale out: Kinesis has quotas which may be a factor in some use cases. For more on throughput quotas of Kinesis, see https://docs.aws.amazon.com/streams/latest/dev/service-sizes-and-limits.html

Kafka vs Kinesis: Data Durability

When it comes to data durability, both Kafka and Kinesis provide reliable and fault-tolerant solutions. However, there are some differences between the two.

Kafka

Kafka provides data durability by replicating data across multiple brokers. Each message is stored in multiple partitions, and each partition is replicated across multiple brokers. This ensures that if one broker fails, the data is still available on other brokers.

Kafka also provides configurable retention policies, which determine how long data is kept in the system. This allows users to balance data durability with storage costs.

Kinesis

Kinesis provides data durability through replication as well. Each data record is stored in three different availability zones (AZs) within a region. This ensures that even if an entire AZ goes down, the data is still available in other AZs.

Kinesis also provides configurable retention periods, allowing users to control how long data is kept in the system.

Comparison

Both Kafka and Kinesis provide reliable data durability through replication. However, there are some differences between the two.

Kafka provides more flexibility in terms of replication and retention policies. Users can configure the number of replicas and retention time according to their needs. On the other hand, Kinesis provides a fixed replication factor and retention period.

Kafka also provides more control over data partitioning, which can be important for certain use cases. Kinesis, on the other hand, abstracts partitioning away from the user, making it easier to use for some applications.

A key difference between Kafka and Kinesis with regards to data durability is how message keys are hashed and stored. With Kinesis, topic shards may be increased on decreased without affecting ordering. In Kafka, if a topic partition number is changed, then ordering guarantees will be affected.

Overall, both Kafka and Kinesis provide reliable data durability solutions, but the choice between the two will depend on specific use cases and requirements.

Kafka vs Kinesis: Use Cases

When it comes to choosing between Kafka and Kinesis, it’s important to consider the specific use case and requirements of your project. Both platforms offer powerful capabilities for streaming data, but they have some key differences that may make one a better fit than the other.

Kafka Use Cases

Kafka is a popular choice for large-scale, real-time data processing and streaming applications. It’s commonly used for:

Log Aggregation: Kafka can collect and store logs from multiple sources, making it easier to analyze and troubleshoot issues.
Messaging: Kafka’s publish-subscribe model makes it a great choice for building messaging systems that can handle high volumes of data.

Stream Processing: Kafka’s ability to process streams of data in real-time makes it ideal for applications that require real-time insights and analytics.
ETL: Kafka can be used as part of an ETL (Extract, Transform, Load) pipeline to move data between systems and applications.

Kinesis Use Cases

Kinesis is a managed service offered by AWS, making it a popular choice for organizations already using AWS infrastructure. It’s commonly used for:

Real-time Data Processing: Kinesis can process large volumes of data in real-time, making it a great choice for applications that require real-time insights and analytics.
Data Ingestion: Kinesis can be used to ingest data from a variety of sources, including IoT devices, social media, and web applications.
Data Analytics: Kinesis integrates with other AWS services, such as Amazon S3, Amazon Redshift, and Amazon Elasticsearch, making it a good choice for organizations that want to build a data analytics pipeline on AWS.

Overall, both Kafka and Kinesis are powerful platforms for streaming data, but they have some key differences that may make one a better fit than the other depending on your specific use case and requirements.

Kafka vs Kinesis: Pricing

When it comes to pricing, both Kafka and Kinesis offer different pricing models that can be tailored to the needs of individual users.

Kafka Pricing

Kafka is an open-source product, and therefore, it is free to use. However, if you want to use Kafka in a production environment, you may need to pay for support from Kafka service provider such as Confluent, Aiven, or Amazon MSK.

Kinesis Pricing

Kinesis is a managed service offered by AWS, and therefore, it is a paid service. Kinesis offers two pricing models: Pay-as-you-go and Provisioned Capacity.

With the Pay-as-you-go model, you pay for the amount of data that you ingest and the amount of data that you process. The pricing for this model is based on the number of shards that you use and the amount of data that you process per hour.

With the Provisioned Capacity model, you pay for a fixed amount of capacity per hour, regardless of how much data you ingest or process. The pricing for this model is based on the number of shards that you provision.

Overall, both Kafka and Kinesis offer flexible pricing models that can be tailored to the needs of individual users. The choice between the two will depend on the specific use case and the budget of the user.

Kafka vs Kinesis: Security

When it comes to security, both Kafka and Kinesis offer robust features to ensure data privacy and protection.

Kafka provides end-to-end encryption, which means that data is encrypted at the producer level and remains encrypted until it reaches the consumer. This ensures that data is protected from unauthorized access during transmission. Additionally, Kafka supports SSL/TLS authentication, which provides secure communication between the client and server.

Kinesis also supports SSL/TLS encryption and authentication. However, Kinesis provides additional security features such as AWS Identity and Access Management (IAM) roles and policies, which allow users to control access to their data streams. Kinesis also provides server-side encryption, which encrypts data at rest, providing an additional layer of security.

MSK supports IAM.

Both Kafka and Kinesis also offer access control mechanisms to ensure that only authorized users can access data. Kafka uses Access Control Lists (ACLs) to control read and write access to topics, while Kinesis uses IAM roles and policies to control access to data streams.

In summary, both Kafka and Kinesis offer robust security features to ensure data privacy and protection. While Kafka provides end-to-end encryption and SSL/TLS authentication.

When to use Kafka or Kinesis?

Both Kafka and Kinesis are often utilized as an integration system in enterprise environments similar to traditional message pub/sub systems. Integration between systems is assisted by Kafka clients in a variety of languages including Java, Scala, Ruby, Python, Go, Rust, Node.js, etc.

Both options have the construct of Consumers and Producers.

For the data flowing through Kafka or Kinesis, Kinesis refers to this as a “Data Record” whereas Kafka will refer to this as an Event or a Message interchangeably.

Key technical components in the comparisons include ordering, retention period (i.e. greater than 7 days), scale, stream processing implementation options, pre-built connectors or frameworks for building custom integrations, exactly-once semantics, and transactions.

An interesting aspect of Kafka and Kinesis lately is the use of stream processing. More and more applications and enterprises are building architectures which include processing pipelines consisting of multiple stages. For example, a multi-stage design might include raw input data consumed from Kafka topics in stage 1. In stage 2, data is consumed and then aggregated, enriched, or otherwise transformed. Then, in stage 3, the data is published to new topics for further consumption or follow-up processing during a later stage.

Kafka and Kinesis Resources

Spark Streaming with Kafka example

Spark Streaming with Kinesis example

Kafka Tutorials

Kafka and Kinesis Terminology

Both attempt to address scale through the use of topic “sharding”. In Kinesis, this is called a shard while Kafka calls it a partition. As mentioned above Kinesis can add or remove shards in existing topics while Kafka can only add addition partitions to topics.

Kinesis Kafka Ecosystem Comparisons

A few of the Kafka ecosystem components were mentioned above such as Kafka Connect and Kafka Streams. Let’s consider that for a moment.

Kafka Connect has a rich ecosystem of pre-built Kafka Connectors. I believe an attempt for the equivalent of pre-built integration for Kinesis is Kinesis Data Firehose. Example: you’d like to land messages from Kafka or Kinesis into ElasticSearch. How would you do that? Yes, of course, you could write custom Consumer code, but you could also use an off-the-shelf solution as well.

As briefly mentioned above, stream processing between the two options appears to be quite different. I’m not sure if there is an equivalent of Kafka Streams / KSQL for Kinesis. Please let me know. I mean, I’m thinking we could write their own or use Spark, but is there a direct comparison to Kafka Streams / KSQL in Kinesis? AWS Glue maybe?

A final consideration, for now, is Kafka Schema Registry. Kinesis does not seem to have this capability yet, but AWS EventBridge Schema Registry appears to be coming soon at the time of this writing.

Hope this helps, let me know if I missed anything or if you’d like more detail in a particular area.