AWS IoT Analytics

Analytics for IoT devices

AWS IoT Analytics is a fully-managed service that makes it easy to run sophisticated analytics on massive volumes of IoT data without having to worry about all the cost and complexity typically required to build your own IoT analytics platform. 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. IoT data comes from devices that often record fairly noisy processes (such as temperature, motion, or sound), and as a result the data from these devices can frequently 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. For example, to determine when to water their crops, vineyard irrigation systems often enrich humidity sensor data with data on rainfall at the vineyard, allowing them to be more efficient with water usage while maximizing their harvest yield.

AWS IoT Analytics automates each of the difficult steps that are 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 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 queries using the built-in SQL query engine, or perform more complex analytics and machine learning inference. IoT Analytics makes it easy to get started with machine learning by including pre-built models for common IoT use cases so you can quickly answer questions like which devices are about to fail or which customers are at risk of abandoning their wearable devices.

AWS IoT Analytics is fully managed and scales automatically to support up to petabytes of IoT data. With IoT Analytics, you can analyze data from millions of devices and build fast, responsive IoT applications without managing hardware or infrastructure.

AWS IoT Analytics Benefits

Easily Run Queries on IoT Data

With AWS IoT Analytics, you can run simple, ad-hoc queries using the built-in IoT Analytics SQL query engine. The service allows you to use standard SQL queries to extract data from the data store to answer questions like the average distance traveled for a fleet of connected vehicles or how many doors are locked after 7pm in a smart building. These queries can be re-used even if connected devices, fleet size, and analytic requirements change.

Run Time-Series Analytics

AWS IoT Analytics also supports time-series analyses so you can analyze the performance of devices over time and understand how and where they are being used, continuously monitor device data to predict maintenance issues, and monitor sensors to predict and react to environmental conditions.

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 that typically include time as a criteria. The raw data is also automatically stored for later processing or to reprocess it for another use case.

Prepares Your IoT Data for Analysis

AWS IoT Analytics includes data preparation techniques that make it easy to prepare and process your data for analysis. IoT Analytics is integrated with AWS IoT Core so it is easy to ingest device data directly from connected devices. It cleans false readings, fills gaps in the data, and performs mathematical transformations of message data. As the data is ingested, 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. You can also use AWS Lambda functions to enrich your device data from external sources like the Weather Service, HERE Maps, Salesforce, or Amazon DynamoDB. For example, you could combine weather data and mapping information to create better information about a device's environment.

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 IoT Analytics console without having to manage any of the underlying infrastructure. Using AWS IoT Analytics, you can apply machine learning algorithms to your device data to produce a health score for each device in your fleet. For example, an auto manufacturer can detect which of their customers have worn brake pads and alert them to seek maintenance for their vehicles.

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

How it Works IoT Analytics v6FINAL

Use cases

Smart Agriculture

AWS IoT Analytics can automatically enrich IoT device data with contextual metadata using 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 that output recommended actions that your devices can take in the field. For example, operators of connected agriculture equipment can use IoT Analytics to enrich humidity sensor data with expected rainfall to optimize the water-efficiency of their automated irrigation equipment.

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 IoT Analytics to predict when heating and cooling systems will fail on connected cargo vehicles so the vehicle can be rerouted 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 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.

Process Efficiency Scoring

With AWS IoT Analytics, companies can build applications that constantly monitor the efficiency of different processes and take action to improve the process. For example, a mining company can increase the efficiency or their ore trucks by maximizing the load for each trip. With IoT Analytics, the company can 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.

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