Customers like Modjoul, Teralytic, and Georgia Pacific are using AWS IoT Analytics to quickly and easily analyse vast amounts of IoT device and sensor data.
Amazon Web Services (AWS), an Amazon.com company, has announced the general availability of AWS IoT Analytics, a fully-managed service that makes it easy to run simple and sophisticated analytics on massive volumes of data from IoT devices and sensors, empowering customers to uncover insights that lead to more accurate decisions for their IoT and machine learning applications. AWS IoT Analytics collects, pre-processes, enriches, stores, and analyses IoT device data at scale so companies can easily identify things like the average distance travelled for a fleet of connected vehicles, or how many doors are locked after work hours in a smart building, or assess the performance of devices over time to predict maintenance issues and better react to changing environmental conditions. With AWS IoT Analytics, customers don’t have to worry about all the cost and complexity typically required to build their own IoT analytics platform.
“AWS IoT Analytics is the easiest way to run analytics on IoT data. Now, customers can act on the large volumes of IoT data generated by their connected devices with powerful analytics capabilities ranging from simple queries to sophisticated machine learning models that are specifically designed for IoT,” Dirk Didascalou, VP, IoT, AWS, said. “As the scale of IoT applications continues to grow at a rapid rate, AWS IoT Analytics is designed to provide the best tools for our customers to mine their raw data, gaining insights that lead to intelligent actions.”
“AWS IoT Analytics is the easiest way to run analytics on IoT data.”
AWS IoT Analytics also has features like a built-in SQL query engine to answer specific business questions and more sophisticated analytics, enabling customers to understand the performance of devices, predict device failure, and perform time-series analysis. Also, AWS IoT Analytics offers access to machine learning tools with hosted Jupyter Notebooks through seamless integration with Amazon SageMaker. Customers can directly connect their IoT data to a Jupyter Notebook and build, train, and execute models at any scale right from the AWS IoT Analytics console without having to manage any of the underlying infrastructure. Using AWS IoT Analytics, customers can apply machine learning algorithms to device data to produce a health score for each device in a fleet, prevent fraud and cyber intrusion by detecting anomalies on IoT devices, predict device failures, segment fleets of devices, and identify other rare events that may have great significance but are hard to find without analytics. And, by using Amazon QuickSight, a fast, cloud-powered business analytics service, in conjunction with AWS IoT Analytics, it is easy for customers to surface insights in easy-to-build visualizations and dashboards.
AWS IoT Analytics can accept data from any source, including external sources using an ingestion API, and integrates fully with AWS IoT Core. Launched in 2015, AWS IoT Core is a managed cloud platform that lets connected devices easily and securely interact with cloud applications and other devices. AWS IoT Analytics also stores the data for analysis, while providing customers the ability to set data retention policies.
