November 2, 2020

How Kafka ensures scalability of IoT applications

Since the IoT buzzword was coined, it has gone beyond turning on lights and TVs from a remote location. IoT applications have expanded beyond consumer applications, and it’s being applied in retail, manufacturing, energy, and other industries.

On this page

An IoT network is no longer confined to a handful of devices. Instead, the IoT infrastructure has to cope with growing numbers of sensors and controllers. The expansion of nodes to the hundreds or thousands pushes conventional IoT networks to the limit.

Kafka for Scalability issues with IoT

The IoT infrastructure is only as feasible as its ability to process incoming data from its sensor nodes. While IoT devices have grown in processing power, the challenge remains on the efficiency of transferring and analyzing the data.

There are two factors that made scalability an issue with conventional IoT infrastructure: variation and volume. In industrial applications, IoT devices are made up of different types of sensors and controllers. Storing and segregation of non-standard messages can be challenging.

Heterogeneous IoT devices need to share data efficiently and be interoperable with each other. The volume of such devices involved further complicates the matters. IoT applications like fleet management and agriculture often involve hundreds of sensor nodes.

How Kafka makes IoT applications scalable

The challenges faced by IoT applications bring Apache Kafka to the forth. Kafka is a distributed messaging system that allows data transfer between groups of publishers and subscribers. It is proven to be reliable, efficient, and flexible, which is essential for IoT scalability.

 Kafka for IoT applications

Make your business a real-time event business

Transforming your business into a real-time event business means embracing agility, responsiveness, and immediacy. By leveraging real-time data, instant communication tools, and live event technologies, you can engage your audience dynamically, deliver personalized experiences, and quickly adapt to changing circumstances. This approach allows you to make timely decisions, enhance customer satisfaction, and create a more interactive and immersive experience. Real-time events drive higher levels of engagement, giving you the edge to react faster to market trends, consumer needs, and opportunities, ultimately leading to more growth.

Book a demo

Fault-tolerant

As IoT nodes grew, failure in relaying data from sensor nodes to the respective processors can disrupt the system. Kafka is built to withstand data crashes as it features automatic recovery from backup partitions. It allows IoT data to be stored and transferred safely.

Compatibility

It isn’t surprising if current IoT devices will go through drastic changes in the near future. IoT is still evolving and with it, developers will have to bridge the gap between obsolete and new protocols. Kafka works with 3rd party technologies, which allows it to function with a wide range of IoT devices.

For example, Kafka works well with MQTT, a message broker designed for low-level communication between IoT devices. In such setups, Kafka takes up the role of processing event streams that are not handled by MQTT.

Kafka's High throughput for scalability

Kafka is not a conventional queuing system. Instead, it is built for event streaming, which enables real-time processing. In IoT, the cloud server relies on the low-latency delivery of parameters for processing. This can be fulfilled with Kafka’s high throughput data pipeline.

Flexible messaging for IoT devices

IoT devices produce different types of data. For example, the cloud server could be receiving parameters like speed, temperature, wattage, and humidity from the sensors. Apps connected to the IoT network may require selected data in different formats.

With Kafka’s decoupled publisher-subscriber system, it is possible to aggregate the input, process, and provide the desired results in formats required by the apps. IoT devices are not confined to a rigid messaging structure.

Conclusion

Kafka seems to be the missing link for scalability in large-scale IoT applications. Its ability to provide reliable, low-latency streaming is crucial for analytics dependent on the hundreds or more connected IoT nodes.

Download the Whitepaper

Download now
Table name
Lorem ipsum
Lorem ipsum
Lorem ipsum

Answers to your questions about Axual’s All-in-one Kafka Platform

Are you curious about our All-in-one Kafka platform? Dive into our FAQs
for all the details you need, and find the answers to your burning questions.

What is scalability in IoT systems?

What Does Scalability in IoT Mean? Scalability in IoT means how easily a system can grow from a small test model to a full working product. In general, when people think of scalability, they often think of online stores that can quickly handle more orders during busy times, like on Black Friday.

What is scalability in Internet?

The ability of a computer application or product, hardware or software, to continue functioning well when its context changes in size or volume to meet a user need.

How do you explain scalability for IoT?

Scalability in IoT refers to a business or system's ability to grow and handle increased demand for connected devices and data. In IoT, scalability means that the system can still function efficiently as more devices are added. A business that can successfully scale its IoT system should benefit from economies of scale, where the cost of managing devices and data is spread across more units, leading to higher profit margins.

Jurre Robertus
Product Marketer

Related blogs

View all
Richard Bosch
November 12, 2024
Understanding Kafka Connect
Understanding Kafka Connect

Apache Kafka has become a central component of modern data architectures, enabling real-time data streaming and integration across distributed systems. Within Kafka’s ecosystem, Kafka Connect plays a crucial role as a powerful framework designed for seamlessly moving data between Kafka and external systems. Kafka Connect provides a standardized, scalable approach to data integration, removing the need for complex custom scripts or applications. For architects, product owners, and senior engineers, Kafka Connect is essential to understand because it simplifies data pipelines and supports low-latency, fault-tolerant data flow across platforms. But what exactly is Kafka Connect, and how can it benefit your architecture?

Apache Kafka
Apache Kafka
Richard Bosch
November 1, 2024
Kafka Topics and Partitions - The building blocks of Real Time Data Streaming
Kafka Topics and Partitions - The building blocks of Real Time Data Streaming

Apache Kafka is a powerful platform for handling real-time data streaming, often used in systems that follow the Publish-Subscribe (Pub-Sub) model. In Pub-Sub, producers send messages (data) that consumers receive, enabling asynchronous communication between services. Kafka’s Pub-Sub model is designed for high throughput, reliability, and scalability, making it a preferred choice for applications needing to process massive volumes of data efficiently. Central to this functionality are topics and partitions—essential elements that organize and distribute messages across Kafka. But what exactly are topics and partitions, and why are they so important?

Event Streaming
Event Streaming
Jimmy Kusters
October 31, 2024
How to use Strimzi Kafka: Opening a Kubernetes shell on a broker pod and listing all topics
How to use Strimzi Kafka: Opening a Kubernetes shell on a broker pod and listing all topics

Strimzi Kafka offers an efficient solution for deploying and managing Apache Kafka on Kubernetes, making it easier to handle Kafka clusters within a Kubernetes environment. In this article, we'll guide you through opening a shell on a Kafka broker pod in Kubernetes and listing all the topics in your Kafka cluster using an SSL-based connection.

Strimzi Kafka
Strimzi Kafka