* IoT Analytics allows you to collect large amounts of device data, process messages, and store them. You can then * query the data and run sophisticated analytics on it. IoT Analytics enables advanced data exploration through * integration with Jupyter Notebooks and data visualization through integration with Amazon QuickSight. *
** Traditional analytics and business intelligence tools are designed to process structured data. IoT data often comes * from devices that record noisy processes (such as temperature, motion, or sound). As a result the data from these * devices can have significant gaps, corrupted messages, and false readings that must be cleaned up before analysis can * occur. Also, IoT data is often only meaningful in the context of other data from external sources. *
** IoT Analytics automates the steps required to analyze data from IoT devices. IoT Analytics filters, transforms, and * enriches IoT data before storing it in a time-series data store for analysis. You can set up the service to collect * only the data you need from your devices, apply mathematical transforms to process the data, and enrich the data with * device-specific metadata such as device type and location before storing it. Then, you can analyze your data by * running queries using the built-in SQL query engine, or perform more complex analytics and machine learning * inference. IoT Analytics includes pre-built models for common IoT use cases so you can answer questions like which * devices are about to fail or which customers are at risk of abandoning their wearable devices. *
*/ package com.amazonaws.services.iotanalytics;