AWS IoT Analytics

Analytics for IoT devices

AWS IoT Analytics is a fully-managed service that makes it easy to run and operationalize sophisticated analytics on large volumes of IoT data without managing hardware or infrastructure. It is the easiest way to run analytics on IoT data and get insights to make better and more accurate decisions for IoT applications and machine learning use cases. IoT data is highly unstructured which makes it difficult to analyze with traditional analytics and business intelligence tools that are designed to process structured data. AWS IoT Analytics automates each of the steps that are required to analyze data from IoT devices.

AWS IoT Analytics filters, transforms, and enriches IoT data before storing it in a time-series data store for analysis. You can setup 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 the processed data. Then, you can analyze your data by running ad hoc or scheduled queries using the built-in SQL query engine, or perform more complex analytics and machine learning inference.

For more information, visit the AWS IoT Analytics documentation page.

AWS IoT Analytics - How it works (3:01)

AWS IoT Analytics benefits

Operationalize your analytical workflows

You supply the analysis, while AWS IoT Analytics automates the execution of your analysis when and where you need it. AWS IoT Analytics will import your custom authored code containers (built in external tools such as Matlab, Octave, R, and others), and execute them on your schedule to generate operational insights.

Easily run queries on IoT data

With AWS IoT Analytics, you can run simple, ad-hoc queries using the built-in SQL query engine. For example, using standard SQL queries to extract data from the data store, you can calculate the average distance traveled of a fleet of vehicles or the number of doors locked in a smart building.

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 your IoT data for analysis

AWS IoT Analytics is integrated with AWS IoT Core to easily ingest device data directly from connected devices, and includes data preparation techniques that make it easy to process your data for analysis. It cleans false readings, fills gaps in the data, and performs mathematical transforms of incoming data. As the data is ingested, AWS IoT Analytics can process it using conditional statements, filter data to collect just the data you want to analyze, and enrich it with information from the AWS IoT Registry or external data sources such as a weather service.

Tools for machine learning

AWS IoT Analytics makes it easy to apply machine learning to your IoT data with hosted Jupyter Notebooks. You can directly connect your 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. With a single click, you can also package your Jupyter Notebook code into an executable container image and execute it on a schedule.

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. With IoT Analytics, you can analyze your entire fleet of connected devices without managing hardware or infrastructure. As your needs change, compute power and the data store automatically scale up or down so you always have the right capacity for your IoT applications and you only pay for the resources that you use.

How it works

Use cases

Predictive maintenance

AWS IoT Analytics provides pre-built templates to help you easily build powerful predictive maintenance models and apply them to your fleet. For example, you could use AWS IoT Analytics to better predict when heating and cooling systems will fail on connected cargo vehicles and service accordingly to prevent shipment damage.

Proactive replenishing of supplies

AWS IoT Analytics lets you build IoT applications that can monitor inventories in real time. For example, a food and drink company can use AWS IoT Analytics to analyze data from their food vending machines and proactively reorder merchandise for the correct machine and item whenever the food supply is running low.

Smart agriculture

AWS IoT Analytics can automatically enrich IoT device data with contextual metadata using the AWS IoT Registry and other public data sources so that you can perform analysis that factors in time, location, temperature, altitude, and other environmental conditions. With that analysis, you can write models delivering recommended actions your devices can take in the field. For example, operators of connected agriculture equipment can use AWS IoT Analytics to enrich moisture sensor data with expected rainfall to optimize the water-efficiency of their automated irrigation equipment.

Process efficiency scoring

With AWS IoT Analytics, you can build applications that constantly monitor the efficiency of different processes and then act to improve the process. For example, a mining company can increase the efficiency of its ore trucks by maximizing the load for each trip. AWS IoT Analytics can be used to identify the most efficient load for a location or truck over time, and then compare any deviations from the target load in real time, and better plan loading guidelines to improve efficiency.