AWS IoT Analytics is a fully managed service that operationalizes analyses and scales automatically to support up to petabytes of IoT data. It can analyze data from millions of devices and build fast, responsive IoT applications without managing hardware or infrastructure.
Features:
- Operationalize analytical workflows: AWS IoT Analytics mechanizes the execution of analysis. AWS IoT Analytics import custom authored code containers, built in external tools such as Matlab, Octave, etc, and execute them on schedule to generate operational insights.
- Easily run queries on IoT data: AWS IoT Analytics can simply run ad hoc queries by using SQL query engine. It also provides a series of non-overlapping, contiguous time windows to perform analysis on new, incremental data.
- Data storage optimized for IoT: AWS IoT Analytics stores the processed device data in a time-series data store that is optimized to deliver fast response times on IoT queries. The raw data is also automatically stored for later processing or reprocessing for another use case.
- Prepares IoT data for analysis: It uses data preparation techniques that streamline the data for analysis. It also supports the time series analysis that helps in analysing performance of devices overtime and understand how and where they are being used. It continuously monitor device data to predict maintenance issues, and monitor sensors to predict and react to environmental conditions.
- Tools for machine learning: AWS IoT Analytics makes it easy to apply machine learning to IoT data with hosted Jupyter notebooks. It can directly connect IoT data to the notebook and build, train, and execute models right from the AWS IoT Analytics console without having to manage any of the underlying infrastructure.
- Automated scaling with pay as you go pricing: AWS IoT Analytics is a fully managed and pay-as-you go service that scales automatically to support up to petabytes of IoT data. It can analyze entire fleet of connected devices without managing hardware or infrastructure.