Both AWS Kinesis and Apache Kafka are data streaming services and are beyond commendable in their own race. But, your experience is solely dependent upon your needs or the use cases you are planning on implementing.
There are instances where people consider AWS Kinesis as a rebranding service of Apache Kafka. It is not evidently true because both have different feature aspects of fulfilling the diverse needs of clients. Hence, this article is to briefly explain to you the core concepts out of Kinesis vs Kafka for better clarity upon how they are different from one another.
Even though AWS Kinesis and Apache Kafka are offering similar data streaming services, the internal functionalities differ from one another. Go through this Kinesis vs Kafka article to know more about the definition, fundamental knowledge, and differences between these two streaming software platforms.
Definition of AWS Kinesis
AWS Kinesis is known for its important capabilities that include video streams, data firehose, data analytics, and data streams. The comparison of Kinesis vs Kafka is on the data streaming capability, so let’s focus upon it on priority. The data streaming capability of AWS Kinesis is meant to collect & process a large amount of data in real-time. This functionality is the same as that of Apache Kafka.
Along with that, AWS Kinesis is also destined to help offer its key potential for streaming the data irrespective of the scale in a cost-effective manner. It also brings up the flexibility of choosing the right tools that will suit your selected application requirements.
To learn more about What Is AWS Kinesis? From Basics to Advanced!
With AWS Kinesis, you don’t have to wait for complete data collection to commence with the processing. Instead, it processes and analyzes the data immediately, right after it arrives, and responds to it instantly.
The architecture of Kinesis Data Streams is of high-level which is as follows:
- Producer commences with data ingestion onto the Kinesis Data Stream, following to which Kinesis offers a producer library for simplifying the application development. As a result, AWS Kinesis will help you achieve high throughput to KDS.
- KDS is usually a set of several shards, and each of them consists of a specific sequence of data records. Every piece of data within the stream consists of a data blob, partition key, and sequence number.
- Kinesis allows users to build applications with its APIs, Client Library (KCL), or data analytics. The consumers obtain records from KDS and commence with further processing.
Definition of Apache Kafka
Apache Kafka, originally developed by LinkedIn, is an open-source data streaming platform donated to the Apache Software Foundation. The entire platform is written with the Java & Scala language. The APIs within Apache Kafka are responsible for allowing the producers to integrate data streams into the record logs.
These logs of records are also known as topics! Each topic is considered as a partition of such logs that is immutable and is ordered. Consumers are meant to subscribe to such topics. The core APIs of Kafka are:
- Within the Kafka Cluster, producer API enables the apps to send data streams to diverse topics.
- Within the Kafka Cluster, consumer API enables the apps to read data streams from diverse topics.
- The Streams API enables the transformation of data streams from input to output topics.
- Connect API enables the implementation of several connectors that pulls data from any application to Kafka or vice versa.
- AdminClient API enables management and inspection of brokers, topics, and other associated Kafka objects.
Apache Kafka is considered as one of the high-performing data streaming platforms that deal with a high volume of data in real-time. It gives out high throughput for both subscribing and publishing aspects. The distributed systems are highly scalable without putting up any downtime in any of the four dimensions.
Even though the system experiences a failure, Apache Kafka intends to cause no data loss and no downtime. But, Kafka requires some form of human support for installing and managing the clusters. There might be some need for additional efforts for users to configure & scale the functionality to meet the availability, recovery, and durability requirements.
AWS Kinesis vs Apache Kafka
As you have now understood the fundamental definition of both Kinesis and Kafka, it is time for you to witness the Kinesis vs Kafka battle on the aspects of their differentiation factors. The differences between these two data streaming platforms are highlighted with respect to different criteria. The battle of Kinesis vs Kafka begins!
1. Data Retention Ability
AWS Kinesis has the potential of data retention for a maximum tenure of 7 days. And Apache Kafka has a longer retention period as the users are enabled to configure these retention periods.
2. Set-up time & Operations
Apache Kafka comparatively takes a bit longer time to set up as compared to AWS Kinesis. Under the Apache Kafka operations, you will need a complete team to look after installing and managing the Kafka clusters of data. Kinesis is a managed platform, and the maintenance becomes easier over it.
Scaling and replication under Kafka are to be taken care of by the users, while the Kinesis users do not have to take much concern about scaling and replication.
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3. SDK Support Potential
Apache Kafka intends to support only Java SDK, whereas AWS Kinesis supports Java, Android, .NET, and Go SDKs. Hence, the flexibility aspects are high with Kinesis.
4. Pricing
Apache Kafka is an open-source data streaming platform that charges no fee for its services. AWS Kinesis has no charges for the set-up, but the users are requested to pay bills depending upon the usability of resources.
5. Website Reviews
As per the website reviews are considered, Apache Kafka has more reviews from customers as compared to AWS Kinesis.
6. Architecture
The architectural differences are important when Kinesis vs Kafka is considered. The key components of Kafka are topics, consumers, and producers, whereas the key components of Kinesis are data streams, consumers, and producers. Kafka data producers push messages to topics, and Kinesis producers push messages onto dedicated data streams.
7. Security
Apache Kafka is more involved with client-side security features such as encrypting data-in-transit amidst the brokers and applications and also supports secure authentication. Kafka also looks after secure client authorization aspects.
Kinesis uses server-side encryption for offering secure operations. The AWS KMS Master Keys are used to encrypt the data stored within the stream. You are permitted to use your own encryption libraries for securing the data before it is put onto the KDS.
Kinesis vs Kafka Comparison Table
| Comparison Criteria | Kafka | Kinesis |
| Definition | Open-source & free data streaming software platform that can run upon local machines. | Paid-platform for collecting and processing a large amount of data. It is a cloud-based service that is not meant to run locally. |
| Data Storage | Apache Kafka partition | AWS Kinesis Shard |
| SDK Support | Java | Java, .NET, Go & Android |
| Period of Data Retention | Longer retention period depending upon configuration set by users. | The data retention period is maximum of 7 days. |
| Necessary Skills | Advanced skills are required. | Basic skills are required. |
| Scope of Customization | Yes | Yes |
| Limitations of performance | Kafka has fewer limitations as compared to Kinesis. | Kinesis writes data synchronously only to 3 machines at one instance. |
| Support | For support assistance, there are videos, meet-ups, and tutorials. | For support assistance, it has an AWS developer center, tutorials, and other such resources. |
| Security | Kafka offers client-side security features. | Kinesis offers data security by allowing you to implement server-side encryption with KMS master keys. |
Check out our blog on AWS Kinesis Data Streams vs AWS Kinesis Data Firehose!
Conclusion
With this, the detailed comparison of AWS Kinesis vs Apache Kafka comes to an end. By now, you must have gained immense knowledge of the practical differences between these two data streaming platforms. Even though they both serve the same purpose, the way of execution is quite different. Therefore, you cannot consider AWS Kinesis as a rebranding product of Apache Kafka. They are different in terms of operations, way of execution, and other such aspects to reach a similar end result. So, analyze the differences and your needs to go for the one that suits you the most.
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