Use streaming data graphs to automatically refresh your data when underlying data changes. Streaming refresh reduces latency, removes the need for scheduling, and provides more predictable refresh behavior.
Required Editions
| Available in: All Editions supported by Data 360. See Data 360 edition availability. |
Unlike scheduled refreshes that rely on defined intervals, streaming data graphs run automatically when source data changes. Streaming updates only the affected data, which improves efficiency, helps maintain consistent performance, and ensures metadata and data remain in sync.
Streaming data graphs reflect updates to your data graph within minutes, not in real time. If your use case requires lower latency than this, use a real-time data graph instead, which is built to serve requests with sub-second latency for supported data sources.
Streaming data graphs are billed only for the volume of updates processed, not for a full daily refresh. This differs from standard data graphs, which incur a daily full-refresh cost regardless of how much data actually changed. As a result, Streaming Data Graphs can reduce costs specifically when the volume of incoming updates is small relative to the total number of records in the data graph. When you create a streaming data graph, it automatically goes through a full refresh to populate the data.
You can edit a streaming data graph after it's created and apply incremental changes without a full rebuild. When you make an edit, only the affected records and relationships that come in the stream henceforth are processed. Use the Refresh Now option for a full rebuild of the data graph.
