Challenges in storing drone data.

Unlike traditional data architectures involving an online transaction processing data system, where atomicity, consistency, isolation and durability guarantees enable proper inventory registration and calculations, and an online analytical processing system with its reporting, temporal aggregation and analysis capabilities, the lines between transactions and analysis blur for near real-time data analysis of drones as both the inventory and the associated processing must continuously adapt. It could probably be compared to data and event pipelines built for large-scale commercial applications such as AirBnb and with the potential to become real-time processing by eliminating the cost for network latency and storage access. 

Flights for drones remain undisturbed whether stationary or linear until the next update from the controller or local flight path change determination. Clearly, the capabilities of the drone units might vary from fleet to fleet. Individual drone, degree of freedom, motion capabilities, and variety of non-flight actions that the unit can take must need to be differentiated so they can be used selectively. When the entire fleet moves the same as a single unit in the fleet, there are far fewer updates to the data stored for the drones than otherwise. With updates varying from few to large scale, rare to frequent, the data and the events generated must be handled for any volume and rate. At all processing modules, virtualizations that cover the variety possible from the model and type of drone units mandate consistency in the data types used to represent them, so that the interface remains clean with just the right levers and validations for the associated concerns. A unified Api is not just an evolutionary step for drone data management but also a necessity from the start. It might be customary to build data pipeline on Spark and Scala that aids bookkeeping and double-entries for tallying so that the accounting information can then be segregated. Drone data and events have a lot of similarity to edge computing and streaming data pipelines and stores. The need for these approaches must be balanced by the hard performance goals for regular routines.

Cloud databases such as Azure Cosmos DB provide such a general purpose and easily programmable starter stores which can scale and keep up its amazing response times for requests. They remain starter stores because there is little forethought to the specializations in routines needed with data access and storage of drone data and metadata. That said, location information that is rapidly updated for drones can be continuously updated and maintained in these cloud databases. There is convenience in adding various dimensions to the same store as warehouses have taught and such a schema works well for drones as well. But as noted earlier, specializations in components may mandate hybrid data architectures that might not all fit nicely within a single product even if the product were a starting point.

In conclusion, drones data architectures must be articulated with the full palette of storage options, microservices, shared architectures, events, pipelines and automation that may become just as big as Master Data Management systems are today. The good news is that this needs to be done only once for multi-tenant systems.