A company is collecting data from several thousand machines globally.
Which software component in the overall architecture is the next destination of the dataflow after the data has been gathered and normalized on the edge data software?
After the data has been collected and normalized on the edge data software, it needs to be sent to a centralized location for storage, processing, and analysis. This is typically done using a software component called a message broker.
A message broker acts as a central hub for all the data collected from different sources. It receives data from the edge data software and then routes it to the appropriate destination, such as a database or dashboard, based on predefined rules. This ensures that the data is delivered to the right place at the right time.
In this scenario, the most appropriate software component for the next destination of the dataflow would be a message broker like Apache Kafka. Apache Kafka is a distributed streaming platform that can handle large volumes of data from multiple sources. It can also handle real-time processing of the data, making it ideal for IoT applications where timely insights are crucial.
While relational databases like MySQL and historian databases like influxDB can be used for storing data, they are not designed to handle the volume and velocity of data generated by IoT devices. They can also be slow to query and retrieve data, making them less suitable for real-time applications.
A dashboard like a Node.js web app can be used to visualize the data and provide insights to users, but it is not a storage or processing component. It would need to receive data from a message broker or a database to display the relevant information.
In summary, a message broker like Apache Kafka would be the most appropriate software component for the next destination of the dataflow after it has been gathered and normalized on the edge data software.