Let's conclude our discussion by comparing the discovered platforms. To evaluate a platform, it is important to take the specific use case into account, but there are three general key points that we should consider:

• Does the data model conform to how we acquire data?
• Does the support for analytics conform to our needs?
• Are the supported devices suitable for our use cases?

In the I-IoT, we acquire data when it changes, so we may need interpolation functions to fill the gaps, as we discovered with the OpenTSDB and KairosDB open-source databases in Chapter 8, Implementing a Custom Industrial IoT Platform. It is important to consider whether for our particular context it is more appropriate to use an analytical cold-path or hot-path. Microsoft has good support for the OPC DA and OPC UA acquisition standards, while AWS is currently developing support for OPC UA on Greengrass.

With regard to the key points mentioned here, it seems that while Azure is a very mature platform for the I-IoT, AWS is filling the gap to integrate standard protocols and OPC UA on Greengrass, and GCP performs strongly with regard to processing data. The U-SQL and the windowing mechanism of Azure analytics seem to be very powerful in the context of the I-IoT as well.

From a cost perspective, which of the three platforms is more suitable will strictly depend on the discount plans on offer. In general, however, Microsoft Azure has the lowest price, while AWS usually comes in the middle.