Understanding Apache Kafka

Fatima Khalid

09 Aug, 2023

Apache Kafka is a popular open-source distributed streaming platform that was developed by the Apache Software Foundation (Kafka, 2023). It is used to build real-time data pipelines and streaming applications that handle large volumes of data.

Introduction

Apache Kafka is a popular open-source distributed streaming platform that was developed by the Apache Software Foundation (Kafka, 2023). It is used to build real-time data pipelines and streaming applications that handle large volumes of data. Apache Kafka is designed to provide scalable, reliable, and fast data services making it a popular choice for data engineers and software developers. In this blog post, we will discuss what Apache Kafka is, how it works, and its pros and cons especially for the autonomous vehicle industry.

What is Apache Kafka?

Apache Kafka is a distributed streaming platform that was first released in 2011. It is built on top of the Apache ZooKeeper, which is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. It is designed to handle high volumes of data and provide real-time access to that data.

Apache Kafka is a publish-subscribe-based messaging system, which means that producers publish messages to topics, and consumers subscribe to those topics to receive messages. It also provides support for multiple consumer groups, which allows consumers to read messages in parallel. This makes it a highly scalable messaging system that can handle large volumes of data with low latency.

Advantages of Apache Kafka

Apache Kafka has several advantages over traditional messaging systems.

Scalability: Apache Kafka is highly scalable and can handle large volumes of data. Kafka can reliably process and store petabytes of data, making it a popular choice for large-scale data processing.

Real-time Access to data: It provides real-time access to data, which means that consumers can receive data as soon as it is produced. This makes it ideal for real-time data processing applications, such as fraud detection, real-time analytics, and monitoring.

Fault-tolerance: Apache Kafka is designed to handle failures and continue to operate without interruptions. Kafka provides a high level of durability, ensuring that data is never lost.

Multiple consumer groups: This feature allows consumers from multiple groups to read messages in parallel. This makes it possible to process large volumes of data in real-time.

Disadvantages of Apache Kafka

While Apache Kafka has several advantages, there are also many challenges to its successful integration with existing systems.

Resource requirement: Apache Kafka requires a significant amount of resources, including memory and processing power. This can make it challenging to deploy on low-end hardware or in resource-constrained environments.

Complexity: Apache Kafka presents a steep learning curve, and it may be difficult for beginners to learn and set up. Kafka requires a good understanding of distributed systems and messaging architectures, which can be challenging for new users.

Scalability: It may not be suitable for small-scale applications or applications that do not require real-time data access. The overhead of setting up a Kafka cluster may outweigh the benefits for small-scale applications.

Apache Kafka and Uncrewed Vehicles

Apache Kafka can provide numerous data streamlining applications to the uncrewed vehicles industry. Unmanned and autonomous vehicles are becoming widely used in a number of applications such as delivery, disaster relief, monitoring and mapping etc. They are routinely deployed to collect huge amounts of data through connected sensors and cameras. This data is then communicated to base stations and other vehicles. When operating in a remote or shared environment, unmanned vehicles need to share data with other entities as well. For example, in a disaster relief scenario, unmanned drones can be used to make 3D maps of the affected area, detect survivors and provide critical supplies. Huge amount of data is collected by swarms of unmanned vehicles and relief workers organize the best response based on this data. This is possible only when a robust data communication infrastructure exists in which the autonomous vehicles, their base stations, and the police and emergency departments of the area are involved.

Apache Kafka can help in processing this data in real-time from uncrewed vehicles, such as drones, self-driving cars, and autonomous robots (Bear, 2017). The data can be processed in real-time, and the results can be used for further analysis and to make decisions that can help the vehicles navigate safely and avoid collisions. Apache Kafka was utilized to demonstrate a connected automotive infrastructure of 100,000 cars in 2020 (Waehner, 2020), (Waehner, 2020). Such an infrastructure can be extended to self-driving cars and other autonomous vehicles to streamline data.

Conclusion

Apache Kafka is a distributed streaming platform that is used for building real-time data pipelines and streaming applications. It is highly scalable, fault-tolerant, and provides real-time access to data. However, it is a complex system of data streamlining hence, it presents a steep learning curve for beginners. It is ideal for large scale systems where resources such as memory and processing are not an issue. Unmanned vehicle applications where large amount of data is being collected and communicated in real time, offer perfect opportunities for the deployment of Apache Kafka.

References

Bear, J. W., 2017. IoT and the Autonomous Vehicle in the Clouds: SLAM with Kafka and Spark Streaming. Inside Machine Learning.

Kafka, 2023. Apache Kafka. [Online]
Available at: https://kafka.apache.org/

Waehner, K., 2020. Apache Kafka in the Automotive Industry. [Online]
Available at: https://www.kai-waehner.de/blog/2019/11/22/apache-kafka-automotive-industry-industrial-iot-iiot/

Waehner, k., 2020. Streaming Machine Learning at Scale from 100000 IoT Devices with HiveMQ, Apache Kafka and TensorFLow. [Online]
Available at: https://github.com/kaiwaehner/hivemq-mqtt-tensorflow-kafka-realtime-iot-machine-learning-training-inference

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