How to do real-time analytics across historical and live data

Todays analytical requirements are putting unprecedented pressures on existing data infrastructures. Performing real-time analytics across operational and stored data is typically critical to success but always challenging to implement.Consider an airline that wants to collect and analyze a continuous stream of data from its jet engines to enable predictive maintenance and faster time to issue resolution. Each engine has hundreds of sensors that monitor conditions such as temperature, speed, and vibration, and continuously send this information to an internet of things (IoT) platform. After the IoT platform ingests, processes, and analyzes the data, it is stored in a data lake (also known as an operational data store), with only the most recent data retained in the operational database.