IoT systems generate a lot of data; while in many cases it is possible to analyze the data at leisure, for certain tasks such as security, fraud detection, and so on, this latency is not acceptable. What we need in such a situation is a way to handle large data within a specified time—the solution—DAI, many machines in the cluster processing the big data (data parallelism) and/or training the deep learning models (model parallelism) in a distributed manner. There are many ways to perform DAI, and most of the approaches are built upon or around Apache Spark. Released in the year 2010 under the BSD licence, Apache Spark today is the largest open source project in big data. It helps the user to create a fast and general purpose cluster computing system.
Spark runs on a Java virtual machine, making it possible to run it on any machine with Java installed...
