The most relevant components that manage the flow of industrial data in the cloud are the following:

• IoT Hub: This is the dispatcher of data and the manager of devices. It checks security and dispatches data to the right data processors (storage, analytics, or queue). Normally, it is implemented with a multi-protocol gateway, such as AMQP, HTTPS, or MQTTS, and a message broker.
• Time Series Database (TSDB): This is the centralized database in which events and data points acquired from sensors are stored.
• Analytics: These work with data to extract anomalies, the machine's health, the efficiency, or generic key performance indicator (KPI). Analytics can work either in stream mode or in micro-batch processing mode. Normally, we use simple, stream-based analytics to evaluate simple rules, and machine-learning analytics or physics-based analytics for more complex analytics working in micro-batch mode.
• Asset registry: This supports additional (static) information, such as the model of the machine being monitored and the operational attributes. This might include the fuel used, the process steps followed, or the machine's status.
• Data lake: This is normally used to support raw data, such as images or log files. Sometimes, it is used to offer storage support for events and measures (time-series). In the data lake, we normally store the outcome of the analytics.
• Object storage: This stores additional information for large files or document-based data. Object storage can be implemented using the data lake.
• Big data analytics: These are not necessarily within the scope of the IoT, but sometimes we need to run big data analytics over the entire fleet. Alternatively, we might be using a huge amount of data to carry out business analysis.