The architecture for IoT solutions is cloud native, microservice, and serverless based. Also, should be built as discrete services that are independently deployable, and able to scale independently.
- Azure IoT Hub offers built-in secure connectivity, telemetry and event ingestion, and bi-directional communication with devices. IoT Hub also offers an entity store that can be used to store device metadata.
- To register and connect large sets of Devices Azure IoT Hub Device Provisioning Service (DPS) can be used.
- Azure Stream Analytics processes large streams of data records as well as process complex rules.
- Azure machine learning enables learning from data and experiences and to act without explicitly programming. Scenarios such as predictive maintenance can be enabled through ML.
- Azure Data Lake is a distributed data store that can persist large amounts of data without the need of defining schema. It is a graet choice for a big data storage. However, It is slightly more expensive than Azure Blob Storage (specifically for write operations), but it is optimized for big data analytics workloads. The database can also be accessed from Hadoop via WebHDFS-compatible REST APIs or using the U-SQL language.
- Azure Data Factory provides an orchestration service to build data pipelines for transformation and movement of data. Data Factory works across on-premises and cloud environments to read, transform, and publish data.
- Power BI enables the creation of models, KPIs, and their visualization through interactive dashboards. It provides a powerful analytics solution for monitoring the performance of processes or operations and can help to identify trends and discover valuable insights.
- Web and mobile apps can be integrated with Azure Active Directory (AAD) for authentication and authorization control.