AWS IoT SiteWise Features

AWS IoT SiteWise is a managed service that makes it easy to collect, organize, and analyze data from industrial equipment at scale.

Use AWS IoT SiteWise to store industrial data generated from your equipment in a fast and scalable time series data store.

AWS IoT SiteWise storage supports two tiers for equipment data: a hot tier optimized for real-time applications and a cold tier optimized for analytical applications. SiteWise helps you to reduce storage cost by keeping recent data in hot tier and moving historical data to a cost optimized storage tier based upon policies.

Hot tier: The hot tier stores frequently accessed data with lower write-to-read latency. You can store in the hot tier data that will be used by industrial applications that need fast access to the latest values of measurements in your equipment. This includes applications that visualize real-time metrics with an interactive dashboard or applications that monitor operations and trigger alarms to identify equipment performance issues.

Cold tier: The cold tier stores less-frequently accessed data that can tolerate higher read latency. Use data from the cold tier to create applications that need access to historical data, such as business intelligence (BI) dashboards, artificial intelligence (AI) and machine learning (ML) training, historical reports, and backups. This tier stores data in an Amazon Simple Storage Service (Amazon S3) bucket in your account, so you can easily integrate it with the rest of your industrial data lake.

AWS IoT SiteWise periodically exports and syncs data from measurements, metrics, transforms, aggregates, asset definitions, and asset model definitions to the cold tier. Regular data sync ensures you have the most updated view of your industrial operations integrated to your Amazon S3 data lake. From there, you can use a host of other AWS services such as AWS IoT Analytics, Amazon Athena, Amazon SageMaker, and Amazon QuickSight to extract better analytical insights from your equipment data and build ML models to optimize your operations.

Use AWS IoT SiteWise to build models of your physical operations that represent your assets, processes, and facilities, which will help you understand industrial data in the context of your equipment. Once your models are created, you can define an asset hierarchy to accurately represent relationships between devices and equipment within a single facility or across multiple facilities.

Map data streams and define static or computed equipment and process properties across all facilities so they're readily available for analysis. Using a built-in library of operators and functions, you can create two types of custom computations: transforms and metrics. You can define transforms that trigger when equipment data arrives and metrics computed at user-defined intervals that can be configured for an asset or rolled up from a group of assets. AWS IoT SiteWise also automatically computes commonly used statistical aggregates—such as average, sum, and count—over multiple time periods (for example, one minute or one hour) for equipment data, transforms, and metrics. Visualize these auto-computed aggregates using SiteWise Monitor web applications, or retrieve them to be used from your industrial applications.

AWS IoT SiteWise includes AWS IoT SiteWise Edge, on-premises software used to collect, organize, process, and monitor equipment data locally before sending it to AWS. SiteWise Edge runs on local hardware such as third-party industrial gateways and computers, or on AWS Outposts and AWS Snow Family compute devices. SiteWise Edge uses AWS IoT Greengrass, which provides a local software runtime environment for edge devices to help build, deploy, and manage applications. SiteWise Edge automates the process of securely connecting to and reading data from your industrial equipment, on-site data servers, or historian databases. SiteWise Edge collects data using multiple industrial protocols provided as pre-packaged connectors for AWS IoT Greengrass, including OPC Unified Architecture (UA), Modbus TCP/IP, and Ethernet/IP.

Once data is collected, you can filter data streams by sampling or comparing against a specified criterion (for example, air temperature above a user-specified threshold), define asset metrics such as Overall Equipment Effectiveness (OEE), or use an AWS Lambda function to customize how the data is processed. After the data is processed, you can sync it with AWS IoT SiteWise storage in the cloud. For longer-term storage and analysis in your industrial data lake, you can send the data to other AWS services such as Amazon S3 and Amazon Timestream. Local applications can also call AWS IoT SiteWise query APIs on the SiteWise Edge software to read asset time series data, computed transforms, and metrics.

To learn more and access getting started resources, visit AWS IoT SiteWise Edge.

In addition to ingesting data with AWS IoT SiteWise Edge, AWS IoT SiteWise supports other data ingestion methods including MQ Telemetry Transport (MQTT) protocol integration with AWS IoT Core. Use the AWS IoT Message Broker to subscribe to a topic that is publishing messages from your industrial equipment, then use the AWS IoT Core Rules Engine to route messages to AWS IoT SiteWise.

AWS IoT SiteWise also uses REST APIs to allow any edge or cloud application to send data to AWS IoT SiteWise.

Configure and monitor edge gateways across all facilities, and view a consolidated list of active gateways through the console or APIs. Monitor gateway health remotely to view the status of all production lines from one place. Using the Amazon CloudWatch Metrics console, you can also view gateway metrics to monitor the health, status, and performance of your gateway resources.

AWS IoT SiteWise allows you to create no-code, fully managed web applications using AWS IoT SiteWise Monitor. With this feature, you can visualize and interact with operational data from devices and equipment connected to AWS IoT services. You can automatically discover and display asset data ingested and modeled with AWS IoT SiteWise. View asset data and computed metrics in near real time, or compare and analyze historical time series data from multiple assets and time periods. Visualize data using line and bar charts, add thresholds, and monitor data against these thresholds. Users can access the web applications from a browser on any web-enabled desktop, tablet, or phone, and sign in with their corporate credentials through a single sign-on (SSO) experience. Administrators can create one or more web applications to easily share access to asset data with any team in their organization and accelerate insights. You can also deploy web applications locally using the AWS IoT SiteWise Edge software, so you can visualize equipment data in real time on the factory floor, even when cloud connectivity is temporarily disrupted.

To assess equipment behavior or identify equipment performance issues, you can define and update alarms, and set alarm notifications using the AWS IoT SiteWise console, AWS IoT SiteWise Monitor, or AWS IoT SiteWise software development kit (SDK). To monitor an asset data property, define an alarm rule to apply (for example, rotations per minute is greater than a user-defined value), select the severity for this alarm definition (for example, severity values of 1, 2, 3, and 4 corresponding to low, medium, high, and critical alerts), and configure the notifications to send when an alarm is triggered (for example, email and SMS). Once an alarm is defined, operators can take action to manage the alarm workflow and acknowledge, snooze, or disable the alarm. You can also configure additional actions to execute other AWS services including AWS Lambda, Amazon Simple Queue Service ((Amazon SQS), and Amazon Simple Notification Service (Amazon SNS) when an alarm triggers (for example, to integrate alarm notifications with your own ticketing or notification systems).

To visualize equipment alarms, analyze alarm information against live and historical data trends, and determine corrective actions to take (for example, scheduling equipment repair). AWS IoT SiteWise Monitor allows you to display and organize alarm data in a customizable web application. This includes a threshold chart to view live or historical trend lines for asset data or metrics against configured alarm definition thresholds, a status timeline chart to visualize the timeline of alarm state changes, and an alarm table that lists all relevant alarms with key information such as alarm rule, asset name, and current alarm state.

Custom edge and cloud applications can use query APIs to easily retrieve asset data and computed metrics from the AWS IoT SiteWise time series data store, or a publish/subscribe interface to consume a near real-time stream of structured IoT data. Custom edge applications can also call the same AWS IoT SiteWise query APIs on the AWS IoT SiteWise Edge software running on premises, retrieving asset data and metrics without relying on cloud connectivity.