AWS IoT SiteWise utilization

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I have questions regarding the utilization of the AWS IoT SiteWise service in a particular scenario involving a control box that gathers readings from various sensors within households. The objective is to monitor these sensor values. However, it has come to my attention that AWS IoT SiteWise imposes a maximum ingestion rate limitation of 10 values per second for each asset, resulting in throttling. I am curious to know if IoT SiteWise was designed solely for scenarios where entities share similarities and possess the same hierarchical structure, allowing for the creation of asset models just once, which can then be reused through the utilization of placeholders within IoT rules. In my specific scenario, I anticipate being required to create separate asset models with different hierarchy for each individual household. Could you kindly confirm if my understanding is correct?

bogdan
asked 8 months ago243 views
1 Answer
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Hi bogdan.

imposes a maximum ingestion rate limitation of 10 values per second for each asset

Per asset property, not per asset. An asset can have many properties.

SiteWise is designed to suit industrial use cases, such as factories and manufacturing. It's optimized to support near real-time operational dashboards for monitoring and alerting. To me, that's a big driver for its selection; if you want near real-time dashboards of sensor data, SiteWise can be a great choice, in terms of both performance and cost (and regardless of whether it's industrial data). For instance, it will generally be cheaper to serve such dashboards from SiteWise than from Timestream.

SiteWise can ingest data streams without any models or assets defined. Where customers do define models, iterative changes are not uncommon. I don't know enough about your use case to understand why each household would have unique models and hierarchy; that is surprising to me.

The effort in creating models and assets is well worth it if you will use features like transforms, aggregations and formulae. If you don't use these, and the focus is more on visualization and monitoring, then you may consider to only ingest the raw data streams. A lesser form of contextualization can be achieved by careful design of data stream aliases.

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Greg_B
answered 8 months ago

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