Amazon Kinesis vs Apache Storm: What are the differences?

Introduction In this article, we will discuss the key differences between Amazon Kinesis and Apache Storm, two popular streaming data processing platforms.

1. Scalability: One major difference between Amazon Kinesis and Apache Storm lies in their scalability. Amazon Kinesis is a fully managed service offered by Amazon Web Services (AWS), which automatically scales based on the incoming data stream. It can handle large volumes of data without any additional configuration or management. On the other hand, Apache Storm requires manual configuration and management of resources for scaling. While it can scale to handle large workloads, the responsibility lies with the user to provision and manage the necessary resources.

2. Ease of Use: Another difference is in the ease of use. Amazon Kinesis provides a simple and user-friendly interface, making it easier for developers to set up and manage data streams. It offers seamless integration with other AWS services, allowing developers to build end-to-end streaming applications easily. Apache Storm, on the other hand, has a steeper learning curve and requires more technical expertise to set up and operate. It does provide greater flexibility and control over the streaming process but can be more complex for beginners.

3. Fault Tolerance: When it comes to fault tolerance, Amazon Kinesis provides built-in fault-tolerant capabilities. It automatically replicates data across multiple Availability Zones, ensuring high durability and availability. In case of failures, it can recover and resume processing without data loss. Apache Storm, on the other hand, relies on the user to implement fault tolerance mechanisms. It provides primitives for building fault-tolerant topologies, but the responsibility for ensuring fault tolerance lies with the user.

4. Integration with Ecosystem: Amazon Kinesis is tightly integrated with the AWS ecosystem, allowing seamless integration with other AWS services like AWS Lambda, Amazon Redshift, or Amazon Elasticsearch. This integration enables developers to easily build scalable streaming architectures with various data processing capabilities. Apache Storm, on the other hand, can be integrated with a wide range of external tools and systems, providing more flexibility in the choice of technologies. It supports integration with databases, queues, and other data processing frameworks.

5. Real-time vs. Near-Real-time: Amazon Kinesis is designed for real-time streaming data processing. It focuses on processing data in near real-time, typically with low latency. It provides capabilities like real-time analytics, real-time dashboards, and real-time processing of data. Apache Storm, on the other hand, is a distributed real-time computation system. It allows processing of streaming data with low latency but may not have the same level of real-time capabilities as Amazon Kinesis.

6. Managed vs. Self-managed: Lastly, a significant difference is in the management aspect. Amazon Kinesis is a fully managed service, where Amazon takes care of the infrastructure, scaling, and maintenance. Users are only responsible for configuring and deploying their applications. In contrast, Apache Storm is a self-managed framework where users have to manage the infrastructure, scalability, and maintenance themselves. This gives users more control and flexibility but also requires more effort and resources.

In Summary, Amazon Kinesis is a fully managed, scalable, and user-friendly streaming data processing service tightly integrated with the AWS ecosystem, while Apache Storm is a self-managed, flexible, and powerful distributed real-time computation framework with greater control but higher complexity.