Increasing digitization has flooded businesses with an enormous amount of data. Although more data usually is seen as more help for businesses, in reality, we often forget that making data useful and drawing insights from it is typically the end operation. There are lots of complexities associated with the storage, modeling, transmission, and processing of Big Data. One of the interesting aspects of data is transmission and processing. Organizations need to have an effectively designed IT infrastructure to move large amounts of data while monitoring factors like information integrity, time, and cost. The classification of data transmission and processing is based on two questions, how data is transferred and when it is processed. There are two classes – Stream Processing and Batch Processing.
In Stream Processing, data is transmitted continuously to a system that processes it in real-time whereas, in Batch Processing, data is collected over time and in migrating to the destination where any operation on the data is performed after all the data are transferred. Each approach has its advantages and disadvantages, for instance, Stream is very time efficient when dealing with simple analysis at the processing endpoint. While Batch data processing allows complex processing it might slow down a business operation. It is important to understand the business requirement and identify the right approach for transmitting and processing data. Lately, Stream Data Processing has become popular due to its real-time processing capability and development in fast computing/processing. In this article, we will discuss how a Stream Processing Infrastructure designs, the main objective is not to discuss configuration details (which are provided in AWS Project Documentation) but to discuss some key terms and concepts to allow implementing the system comfortably using the official document.