ArcGIS Analytics for IoT allows organizations to ingest, visualize, analyze, and act on data from sensors. It also enables the processing of high-volume historical data to gain insights into patterns, trends, and anomalies. Remote monitoring of assets, predictive maintenance, and process optimization are a few of the benefits you can gain from your IoT data.
ArcGIS Analytics for IoT is updated regularly, below is a quick overview of some highlights in the August 2020 release:
- Feeds and data sources – XML is now supported as an input data format and a new data source was added allowing you to load data from Azure Cosmos DB.
- Outputs – A new output type allows you to push data to an HTTP endpoint as the result of a real-time or big data analytic. In addition, the Feature Layer output now supports editor tracking.
- Analytics – Significant performance improvements to real-time analytics as well as enhancements to the user interface, analytic metrics, logs, and more.
Now, let’s take a deeper dive into each of these exciting new enhancements!
Feeds and data sources
A feed is a real-time stream of data coming into ArcGIS while a data source loads stored data for use in a real-time or big data analytic. Integrating with popular data and messaging platforms is one of the key capabilities of Analytics for IoT.
With this release, XML is now supported as an input data format for the following feed and data source types:
- Feeds – Azure Event Hub, Azure Service Bus, Website (Poll), RabbitMQ, WebSocket, Kafka, MQTT, AWS IoT, and Endpoint (Receive).
- Data sources – Amazon S3, Azure Blob Store, and Website (Poll).
In addition, a new data source was added that allows you to load data from Azure Cosmos DB.
An output is a result or action to be taken as the final step in a real-time or big data analytic. Analytics can emit data to a variety of different destinations, including storing data to a feature layer, sending an email, writing to a cloud store, and pushing to a third-party system for device actuation.
A new output type at this release allows you to push data to an HTTP endpoint as the result of a real-time or big data analytic. In addition, the Feature Layer output now supports editor tracking. Your incoming data can now automatically be captured with creation date, last edited date, and the creator and editor user details. The creator and editor username can come from incoming fields in your data, or be automatically populated as the user who is running the analytic.
The power of Analytics for IoT can be realized in the real-time and big data analytic capabilities that can be performed on your IoT data. With this release several new features and enhancements are available including:
- Real-time analytic performance improvements that now consume significantly less compute for equivalent velocities of processing.
- The Find Similar Locations tool was enhanced to support specifying an extent for the reference locations.
- User interface enhancements including truncating long model node labels and displaying the full label on hover over a node. Also, auto layout in the model no longer causes crossing lines for tools that have two inputs.
- Analytics metrics enhancements and fixes including all model nodes in a big data analytic report the correct number of processed features. In addition, autoscaled real-time analytics report the increased compute resources.
- Log enhancements for when an analytic successfully creates an output feature or stream layer.
- Enhancements to prevent output feature layers from being deleted if the analytic that created them is still running.
- Performance enhancements to automatically adjust certain settings if a big data analytic encounters memory errors.
In addition to the existing options for visualizing your IoT data, new rendering capabilities were added for output map image layers. When rendering raw features, manual class breaks can now be set to vary feature symbol colors based on values from an attribute.
And lastly, administrators of an organization can now share Analytics for IoT user created items with designated groups.
For a complete list with more details, check out the what’s new topic in the documentation.
To learn more about Analytics for IoT, peruse the available resources to access product videos, quick lessons, documentation, and more!