According to Tara Walker in a company blog post, this is about providing a way to manage all of that data. “With the AWS IoT Analytics service, you can process messages, gather and store large amounts of device data, as well as, query your data. Also, the new AWS IoT Analytics service feature integrates with Amazon Quicksight for visualization of your data and brings the power of machine learning through integration with Jupyter Notebooks.”
That last part could come in handy when building a data model based on the data coming from your sensors or devices, and AWS also announced SageMaker, a tool for building, deploying and managing data models that also includes support for Jupyter Notebooks.
Because of the sheer volume of data involved, Amazon created a dedicated service instead of letting customers deal with IoT data in a more general tool like QuickSight. That could be because it requires a predictive element, rather than one that looks back at what happened.
For example, you could use IoT analytics in an industrial setting to determine when a machine might require maintenance before it actually breaks down. That would allow you take down the machine on your terms, rather than as an emergency situation.
The IoT analytics tool lets you gather, store and then query the messages coming from your IoT sensors, while extracting specific sets of data on a regular basis.
(Techcrunch)
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