The following are important considerations for ArcGIS organization administrators when working with ArcGIS Velocity.
Consider which users need real-time privileges
Velocity allows users to create feeds and real-time analytics to work with tracking and observation data. Both feeds and real-time analytics are continuous or real-time tasks, meaning they are always running and consuming capacity. Consider the key feeds and real-time analytics needed for your organizational workflows and limit the privileges for these items to users who manage those processes. Administrators can view, edit, start, and stop items created by other users. For more information on user privileges, refer to creating roles and assigning users.
A common pattern is running a defined set of feeds and associated real-time analytics that process and store the incoming data in a feature layer.
Encourage users to proactively manage their real-time items
As both feeds and real-time analytics are tasks that are always running and consuming capacity, it is important to proactively manage these items. Encourage users to stop feeds or real-time analytics that are not needed or that have been set up largely for testing and development. Administrators can view, edit, start, and stop items created by other users.
Review the actively running real-time items
On a periodic basis, it is recommended that you review the real-time tasks being published with Velocity in case there are excess tasks running that are not needed. On any of the item list pages, choose to view the Organization Content option instead of the My Content option. When you view organization content, you can inspect certain details of user items such as a feed's schema or item logs and you can stop any running tasks. This allows you to free up processing capacity if necessary. For more information, refer to feed and analytic management.
Apply shorter data retention time periods
When creating output feature layers, users can apply data retention policies that range from one hour to one year. As a best practice, consider both the available storage and the needs of your users and use cases.
When storing incoming data over time, the recommended best practice is to test and observe how the feature layer grows over several days. The percentage of memory used by a feature layer can be explored on the Memory Utilization page in the app. Set the data retention time period so that the feature layer does not consume an excessive portion of your overall storage before older data is deleted.
Additionally, consider the actual time period for which your data is relevant to your day-to-day workflows versus occasional analysis workflows. Set the time period for which you need data available for immediate exploration and visualization as the data retention policy. If you need older data for occasional analysis, you can choose to export the data to the archive before it being purged from the feature layer.
Applying shorter data retention policies for datasets that grow in real-time maximizes the remaining feature storage available for analytical results.