When you need to analyze data in real-time to power analytical products or dashboards, its mission critical to use ETL so that the data is cleansed and processed. Here I would opt Apache Spark, which is a popular tool for stream processing because it can handle large data volumes and distribute the processing across multiple computing instances for faster results.
During a major overhaul of our user-interface, stream processing was essential for our data pipeline. With the sheer volume of user interaction data generated in real-time, we had to find a solution that could capture, process, and analyze instantly, for which we leveraged the power of Google's Cloud Dataflow. It effortlessly handled the surge in data, giving us a crystal-clear mapping of user behaviors and pain points, thereby letting us tailor our interface to perfection in record time.
Certain industries adjust so quickly that to focus solely on batch processing would be a mistake. Recruiting is one of these fields; if I didn't stream process, I'd be left behind my competitors. Take remote work for instance: It completely changed the hiring landscape in a matter of weeks during Covid-19. An advanced platform like Striim allowed me to investigate shifts in real time, making changes and testing my process along the way. More and more industries are moving in this direction, where the status quo is more like old news than trusted and true. Developing techniques and applications for stream data processing will soon be imperative for all businesses. But historical information isn't going away anytime soon, so keep your records intact. It's about evaluating long- and short-term trends simultaneously. Travis Hann Partner, Pender & Howe https://penderhowe.com/toronto-executive-search/