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.
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.
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.
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.
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 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.
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.
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.
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