Many analytics and machine learning use cases connect to data stored in data warehouses or data lakes, run algorithms on complete data sets or a subset of the data, and compute results on cloud architectures. This approach works well when the data doesn’t change frequently. But what if the data does change frequently?
Today, more businesses need to process data and compute analytics in real-time. IoT drives much of this paradigm shift as data streaming from sensors requires immediate processing and analytics to control downstream systems. Real-time analytics is also important in many industries including healthcare, financial services, manufacturing, and