ArcGIS Analytics for IoT uses data sources to load historical observation data or other stored features into an analytic for processing.
- In big data analytics, data sources are the primary source of data to be processed or analyzed.
- In real-time analytics, data sources load historical data which functions as ancillary data to enrich or filter observations with tools such as Join Features, Calculate Distance, and more.
What is historical data?
Historical data is static or near real-time data. This could include anything from records that were collected in the past two minutes, to billions of records and observations collected over the past few decades. The client or consumer of the information (in this case a big data or real-time analytic) is not actively subscribed to it or receiving data from it in real-time. This means that the data is only loaded each time the analytic is run.
Often, a feed and real-time analytic will be utilized in conjunction to ingest and store observational data to a feature layer or a different output. Then, a big data analytic can be configured to load and process this stored observational data at a defined interval.
Examples of historical data
- A feature layer containing hurricane path polylines between 2000 and 2010.
- Shapefiles containing vehicle location observation points over time.
- A GeoJSON file of the 2010 census tract polygons.
- An RSS webpage with earthquakes in 2017.
- Delimited text files representing city police traffic stop points in 2019.