Reading the available material on data-in-motion reminds me of when I first read about data lakes over data warehouses, or NoSQL over SQL: the urgency of the former, and outright danger of the latter, are both overblown. Put simply, data-in-motion provides real-time insights. Most of our analytics efforts across data science spheres apply to stored data, be it years, weeks, or hours old. Taking a look at data-in-motion means not storing it prior to analyzing it, and extracting insights as the data rolls in. This is one workstream of dual efforts that tell us the whole picture: historical data providing insight on what happened and potential to train for future detection, and real-time data to get what’s happening right now.
Churchward (2018) argues a fair point here: once data is stored, it isn’t real-time by definition. But taking that argument to a logical extreme by asking whether we would like to make decisions on data three months old is a stretch. While it is true that matters such as security and intrusion detection must have real-time detection, categorically dismissing data-at-rest analytics is reckless. It vilifies practices that are the foundation of any comprehensive analytics strategy. Both data-at-rest and data-in-motion are valuable drivers of any business intelligence effort that seeks to paint a total picture of any phenomena.
There are, of course, less frantic cases to be made for data-in-motion. Patel (2018) illustrates a critical situation on an oil drilling rig, in which information even a few minutes old can be life-threatening. In this case, written for Spotfire X, there may be some confusion of monitoring versus analytics. The dashboard shown on the website and written scenario paint more a picture of monitoring and dashboarding than the sort of analytics we would consider deploying Spark or Kafka for. I don’t need a lot of processing power to tell me that a temperature sensor readings are increasing.
Performing real-time analytics on data-in-motion is an intensive task, requiring quite a bit of computing resources. Scalable solutions such as Spark or Kafka are available but may eventually hit a wall. Providers such as Logtrust (2017) differentiate themselves as a real-time analytics provider by pointing out the potential shortfalls of those solutions and offer a single platform for both data-in-motion and data-at-rest.
Churchward, G. (2018). Why “true” real-time data matters: The risks of processing data-at-rest rather than data-in-motion. Retrieved from https://insidebigdata.com/2018/03/22/true-real-time-data-matters-risks-processing-data-rest-rather-data-motion/
Logtrust. (2017). Real-time IoT big data-in-motion analytics.
Patel, M. (2018). A new era of analytics: Connect and visually analyze data in motion. Retrieved from https://www.tibco.com/blog/2018/12/17/a-new-era-of-analytics-connect-and-visually-analyze-data-in-motion/