Internet of Things (IoT) is increasingly becoming a foundation stone to corporations achieving the next level of operational efficiency, and helping businesses in making decisions in real-time. According to Gartner, “By 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions.”
Multiple tools have emerged to address the huge amount of data that is being produced by these devices to undertake real-time processing problems. These solutions range from Mapreduce to Apache Spark.
These platforms can process real-time streaming data on distributed resources with very little latency and high throughput. Without the emergence of these platforms, the use cases that IoT devices can address would not be possible.
Apache Kafka has use cases right across the real-time stream analytics ecosystem and is not limited to the IoT domain. It can be deployed in industries like finance, logistics, manufacturing, etc, any industry that needs high-quality analytics along with speed and volume.
A typical IoT Analytics application that needs real-time streaming of data is used in logistics hubs where the connected devices and sensors need to work like a Beehive. Hub and Spoke messaging and nanosecond updates that track the movement of packages and other goods as they flow through the various nodes in the network.
A centralized hub sits in the middle and Apache Kafka with its superior data collection and analysis can provide the much-needed depth as well as the bandwidth for such systems.
It must be noted that logistics systems using IoT need both real-time pure-play updates as well as more detailed analytics. For instance, the nodes need a continuous stream of data as does the hub, and both need to communicate with each other in real-time.
Also, Smart Analytics is needed to monitor performance and take corrective actions accordingly. Telemetry that is a feature of Apache Kafka enables it to be used in such cases. It does not need repetition that scale and fault tolerance is a must for any logistics IoT systems as well as the much needed real-time messaging that Apache Kafka provides.
The messaging feature of Apache Kafka is also notable for its ability to order and buffer messages waiting for acknowledgment from the users as well as responding to requests all in real-time. Its Data Stores are based on sound architectural designs that support multi point-based IoT devices.
The future belongs to IoT with its diverse uses ranging from homes to Smart City systems to virtually anything that is connected online. Apache Kafka is best placed to harness the power of such systems driven as it is by the features described earlier. To conclude, Apache Kafka is the Go-To application of choice for Real-Time Analytics Streaming of IoT.
It is important to understand that Apache Kafka is the most suitable solution for every nature of data that is emanating from IoT devices and across use cases. In cases where the data load is variable Spark might not be the best option and Apache Flink might be a better option because of its capability for higher latency