AWS IoT SiteWise

A managed service for collecting, organizing, and storing industrial equipment data using asset models and time-series measurements, with optional on-premises SiteWise Edge collectors. It provides APIs to query measurements and computed metrics and integrates with other AWS analytics and storage services. It typically sits between operational technology (OT) systems and cloud analytics or monitoring applications.

🗂 Resource Category

Internet of Things • Analytics

🧠 Exam Memory Hook

Think: "Industrial telemetry + asset models + time-series store = AWS IoT SiteWise"

📖 Ownership

Classification: AWS Managed Service

AWS responsibilities: AWS operates and maintains the cloud control plane, managed time-series storage, APIs, service availability controls, and the SiteWise service platform including backend scaling and platform security for the cloud components. AWS secures the underlying physical infrastructure, network isolation of the managed service, and the managed runtime that runs the cloud service. AWS provides service-level updates and patches for the cloud service platform and manages multitenant infrastructure that hosts the service.

Customer responsibilities: The customer configures asset models, measurement mappings, access control policies (via IAM), data retention/configuration, and monitoring of their SiteWise resources and metrics; they deploy, configure, and manage SiteWise Edge on on-premises hosts when used. The customer is responsible for securing credentials, defining authorization, securing edge-host networks and devices, and integrating SiteWise data with downstream analytics, storage, or visualization tools.

Patching responsibilities: AWS patches and maintains the cloud service platform, underlying physical infrastructure, and managed runtime that runs AWS IoT SiteWise in the AWS region. The customer is responsible for patching and maintaining any on-premises or edge hosts, guest operating systems, and any SiteWise Edge runtime or gateway instances they deploy, as well as application libraries, dependencies, and customer-deployed applications that consume SiteWise data.

🏗 Typical Architecture

💡 Top 5 Features

Managed asset modeling to define equipment, hierarchies, and measurement mappings for industrial systems.
Ingests and stores time-series measurements and historical data for assets in a managed, queryable store.
SiteWise Edge for on-premises data collection, local buffering, and edge processing before cloud transfer.
Built-in computed metrics and transforms to produce derived values and aggregate measurements at ingest or on demand.
REST and SDK APIs for querying asset properties, measurements, and historical time-series data for integration with apps.

✅ Top 5 Use Cases

Normalize telemetry from heterogeneous OT devices into standardized asset models so downstream analytics can use consistent schemas.
Collect on-premises sensor and PLC data with SiteWise Edge and reliably transfer time-series data to the cloud for long-term storage and analysis.
Compute operational KPIs and rollups close to ingestion to reduce downstream processing and provide near real-time monitoring.
Provide time-series data and asset context to visualization tools and dashboards via SiteWise APIs for operator and engineering views.
Stream or export historical measurement data from SiteWise to data lakes or analytics services to enable batch analytics and reporting.

🏗 Architecture Placement

AWS IoT SiteWise is normally deployed between operational technology (OT) systems and cloud analytics or monitoring layers; on-premises devices send telemetry to SiteWise Edge or gateways, which forward data to the regional SiteWise service. The cloud service stores time-series measurements and asset models and exposes APIs that analytics, visualization, and monitoring applications call. Depending on configuration, SiteWise can export data to object storage or other analytics services for long-term retention or advanced analysis.

🎯 Commonly Used With

- AWS IoT Core
- AWS IoT Greengrass
- Amazon S3
- Amazon CloudWatch
- AWS Identity and Access Management

🌍 5 Real-World Examples

A manufacturing plant uses SiteWise Edge to collect PLC telemetry, apply asset models, and send historical measurements to the cloud for factory-wide performance monitoring.
A utilities operator centralizes sensor measurements from distributed substations into SiteWise to provide engineers with consistent asset hierarchies and historical metrics for troubleshooting.
An energy producer maps turbine instrumentation to SiteWise asset models and computes derived metrics to surface abnormal operating conditions to operators.
A pharmaceutical facility consolidates equipment telemetry into SiteWise to feed quality-control dashboards that correlate process parameters with production batches.
A logistics terminal collects conveyor and sorter telemetry into SiteWise to monitor equipment runtime and aggregate usage metrics for operations reporting.

🎓 AWS Exam Clues

Look for requirements mentioning industrial asset models, time-series measurements, or hierarchical equipment context.
Choose SiteWise when questions require on-premises collection with local buffering or edge processing for OT systems.
Prefer SiteWise for workloads needing managed time-series storage plus asset context rather than raw MQTT message routing alone.
Consider integration points with analytics or storage (for example exporting measurement history) when long-term retention or batch analytics are required.
Remember that access and authorization to SiteWise resources are controlled via IAM and the customer configures those policies.

📝 Quick Revision

AWS IoT SiteWise collects, models, and stores industrial equipment time-series data and provides APIs and edge options for on-premises collection. Use it when you need asset-aware telemetry ingestion, local edge buffering, and managed time-series storage; plan for customer responsibility over asset modelling, access control, and edge host maintenance.

🏷 Keywords

asset model • time-series • measurement • SiteWise Edge • gateway • asset hierarchy • computed metrics • historical store • OPC-UA • MQTT • data export • asset APIs