In this post, we’ll explore unbounded data, what data stream processing is, its characteristics, workflow, and why should we leverage the cloud for it?
Join the DZone community and get the full member experience. Today’s data is generated from an infinite number of sources – IoT sensors, database servers, application logs. It is almost impossible to regulate the structure, data integrity, or control the amount or speed of data generated. While traditional solutions are built to ingest, process, and structure data before it can be acted upon, streaming data architecture adds the ability to consume, persist to storage, enrich, and analyze data in motion. As more and more data comes from a variety of event sources, there is a need for technology to collect, process data, and extract real value from data in real time. Data should now be delivered much faster than ever before. Streaming data is used in many industries. Strictly speaking, actually, every data can be considered as a stream. Some of the most popular use cases are listed below: Bounded data is finite and has a discrete start and end. You can associate Bounded data with batch processing. For example, sales data for a company is collected daily. Then it is uploaded to the database every week, every month, or even every year. The analysis is run so that data insights are gained and outputs created through a batch process. Unbounded data is infinite, having no discrete beginning or end. Unbounded data are typically associated with stream processing. As an example, sensors continually collect real-world data on temperature, speed, location, and more. Data collection never stops. This is a 24-hour continuous process. Stream processing combines the collection, integration, and analysis of unbounded data. Stream processing delivers unbounded data continuously, rather than waiting for a batch job to complete at the end of a day or a week.
