Apache Storm vs. Apache Kafka Stream

Apache Storm and Apache Kafka are both open-source frameworks commonly used to process real-time streaming data in big data applications. While Apache Kafka is a distributed message broker that utilizes topics and partitions to handle large amounts of data within seconds, Apache Storm is a fault-tolerant distributed framework that processes data from different sources like HBase and Kafka.

Let’s take a look at some vital differences between the two frameworks.

Development history

1. Storm development and acquisition:

   1. Storm was initially developed outside of any corporate backing.

   2. Twitter acquired Storm, recognizing its potential for real-time data processing.

   3. Later, Storm was open-sourced and became an Apache project.

2. Kafka development and acquisition:

   1. Kafka, a distributed streaming platform, was originally developed by LinkedIn to handle real-time data feeds and analytics.

   2. Apache acquired Kafka, recognizing its scalability and fault-tolerance features.

   3. Kafka also became an Apache project.

3. Industry impact:

   1. Both Storm and Kafka, originating from independent development efforts, found their homes within the Apache Software Foundation.

   2. This transition solidified their positions as industry-standard tools for stream processing and messaging.

Type of framework

Apache Storm and Apache Kafka Streams are two prominent frameworks for real-time stream processing. Each leverages distinct messaging systems to handle data streams effectively. Key differentiators are listed below:

1. Apache Storm:

   1. Known for its real-time processing capabilities.

   2. Utilizes a dedicated real-time messaging system within its architecture.

   3. Processes data streams as they arrive, facilitating near-instantaneous analysis and response to changing data.

   4. Excels in real-time analytics and continuous computation.

2. Apache Kafka Streams:

   1. Operates within the Kafka ecosystem.

   2. Leverages Kafka’s distributed messaging system for data transport and coordination.

   3. Kafka’s messaging system provides fault-tolerant, scalable, and durable message storage.

   4. Ideal for building robust streaming applications.

   5. Integrates stream processing seamlessly within Kafka’s infrastructure, ensuring high availability and reliability for large-scale streaming workloads.

3. Comparison:

   1. Both frameworks cater to real-time stream processing needs.

   2. They differ in their underlying messaging systems and architectural approaches.

   3. Apache Storm offers near-instantaneous processing and is suitable for real-time analytics.

   4. Apache Kafka Streams ensures high availability and reliability within the Kafka infrastructure, making it suitable for large-scale streaming workloads.

Data processing

Apache Storm and Apache Kafka Streams are both powerful frameworks for real-time stream processing, each with its unique strengths.

1. Apache Storm:

   1. A distributed stream processing framework.

   2. Typically employed for processing streams in the form of micro-batches.

   3. Processes data in real-time as it arrives, allowing for continuous analysis and rapid responses to changing data.

   4. Focuses on real-time stream processing with micro-batches.

2. Apache Kafka Streams:

   1. Functions as a message broker within the Kafka ecosystem.

   2. Leverages Kafka’s distributed messaging system for data transport and coordination.

   3. Allows for efficient handling of small batches of data.

   4. Operates within the Kafka infrastructure, enabling seamless integration with Kafka’s messaging system.

   5. Ensures scalable and fault-tolerant small-batch processing.

3. Comparison:

   1. Apache Storm is designed for real-time processing with micro-batches, facilitating continuous analysis and rapid responses.

   2. Apache Kafka Streams, on the other hand, is tailored for message brokering within the Kafka ecosystem, ensuring seamless integration and robust performance.

Data storage

Apache Storm and Apache Kafka Streams are two distinct frameworks for real-time stream processing, each with its unique approach to data handling.

1. Apache Storm:

   1. Operates as a real-time stream processing framework.

   2. Does not store data; data flows continuously from input to output streams.

   3. Enables rapid analysis and response to streaming data within Storm’s processing topology.

   4. Prioritizes real-time processing without data persistence.

2. Apache Kafka Streams:

   1. Incorporates data storage as an integral part of its architecture.

   2. Leverages a file system, such as EXT4 or XFS, to persist data streams.

   3. This storage mechanism ensures fault tolerance and scalability.

   4. Capable of handling large volumes of data efficiently.

   5. Provides durable storage capabilities, enabling the development of robust streaming applications with built-in fault tolerance.

3. Comparison:

   1. Apache Storm focuses on real-time processing without data persistence, making it suitable for scenarios requiring immediate data analysis and response.

   2. Apache Kafka Streams, on the other hand, emphasizes durability and scalability by incorporating data storage, making it ideal for building fault-tolerant and scalable streaming applications.

Dependencies

Apache Storm and Apache Kafka are both powerful tools for stream processing, but they differ significantly in their operational dependencies and architecture.

1. Apache Storm:

   1. Operates independently without relying on external dependencies for its core functionality.

   2. Data flows seamlessly through Storm’s processing topology without the need for external coordination.

   3. Offers self-contained stream processing capabilities.

2. Apache Kafka:

   1. A distributed streaming platform.

   2. Relies on Zookeeper for managing its server infrastructure and coordinating read and write operations.

   3. Zookeeper ensures the consistency and reliability of Kafka’s distributed messaging system.

   4. This reliance on Zookeeper underscores Kafka’s distributed nature, enabling fault-tolerant and scalable stream processing.

3. Comparison:

   1. Apache Storm provides self-contained stream processing, making it simpler to deploy without the need for external coordination mechanisms.

   2. Apache Kafka’s dependence on Zookeeper highlights its robust distributed architecture, which is essential for maintaining fault tolerance and scalability in complex environments.

Data security

Apache Storm and Apache Kafka Streams both offer robust security features, but they approach data security differently.

1. Apache Storm:

   1. Provides options for securing data within its processing topology.

   2. Features include role-based access control (RBAC), encryption, and authentication mechanisms.

   3. Ensures data processed by Storm remains highly secured, with access restricted to authorized users and data encrypted to protect against unauthorized access.

2. Apache Kafka Streams:

   1. Focuses primarily on stream processing within the Kafka ecosystem.

   2. Leverages Kafka’s robust security features for data protection.

   3. Kafka’s security capabilities include SSL/TLS encryption, authentication, and authorization through mechanisms like ACLs (Access Control Lists) and Kerberos.

   4. Kafka Streams inherits these security features from Kafka and provides additional layers of security through integration with external security frameworks.

3. Comparison:

   1. Apache Storm offers built-in security measures such as RBAC, encryption, and authentication directly within its processing topology.

   2. Apache Kafka Streams relies on Kafka’s comprehensive security features but also supports further security enhancements through integration with external frameworks.

Test yourself

Before moving on to the conclusion, test your understanding.

Conclusion

While both Apache Kafka Streams and Apache Storm are powerful tools for real-time stream processing, they exhibit significant differences in their architectures, semantics, fault tolerance mechanisms, scalability, and integration with external systems. Apache Kafka Streams, tightly integrated with the Kafka ecosystem, excel in providing seamless, horizontally scalable processing with strong fault tolerance and exactly-once semantics. On the other hand, Apache Storm, as a standalone framework, offers greater flexibility in language support and deployment options, making it suitable for diverse streaming use cases. Understanding these distinctions is crucial for selecting the appropriate solution that best aligns with the specific requirements and constraints of a given streaming application.