A few years ago I was listening to a presentation from Microsoft where they touted a customer that had real time analytics with Analysis Services. Since my exposure had been that cubes needed to be processed, a resource intensive process, I was wondering what this meant. Imagine my surprise to hear that the cube was being updated within 5 seconds of the changes in the OLTP system. Talk about real time.
Of course, the definition of real time isn’t really well known. We linked a piece in this week’s newsletter that looks at a survey of companies and their investment in real time, streaming, data movement pipelines. The definitions from companies about real time range from sub-second to 24 hours. I’m sure I think that 24 hours is real time, but if you’re used to processing cubes weekly, maybe that feels real time. I tend to see real time on the order of minutes, maybe a few tens of minutes. Once we get to hours, that feels more like batch processing, albeit fairly rapid batch work.
I am sure there are systems that need real time analysis, especially in some areas where decisions must be made quickly. As we move to more and more machine learning algorithms and automated intelligence, there will certainly be more call for real-time data movement. Yet another opportunity for data professionals, especially those that work with ETL pipelines. There will be tough problems, not only with moving data, but tracking lineage, recovering from issues, even weeding out bad data quickly.
However, that’s an opportunity for you, not me. I bet there are a few data professionals out there that feel the way I do. There are great challenges in solving real time problems, and building systems that can handle high volumes of data. The thing is, I value my sleep. I value not working all-nighters, I value a balance in my life. While I find the problems fascinating and the money involved tempting, I prefer to work on more pedestrian systems.
I’m glad there are people that want to work on very difficult problems, and I wish you all the best in taking advantage of these opportunities. I hope you’re well paid, and you have a great time building these impressive systems. I also hope to read about some of the amazing things you do, so please, share the knowledge where you can. This is a great, exciting time to work with data, and it’s one that I continue to enjoy every day.