No Start/Stop on event stream ?
Hi, I built an eventstream with two targets. The problem is: Once I completed the configuration of the first target, the ingestion started, leaving the 2nd target behind. Should the eventstream have a start/stop button to ensure both targets will be in sync? Am I missing something, or is this a feature request? Kind Regards, Dennes
How to feed Streaming data to Custom App in Eventstream in Realtime Analytics Fabric using Fabric
Hi, I want to stream data stored in a csv file in Azure Storage account by feeding data to Custom App Event Hubs data source in Eventstream in Real time Analytics in Fabric. I want to use a Fabric solution to implement this. Any help on this would be appreciated. Thanks.What is KQL db in fabric generally used for storing, is it historical data or only streaming data?
Hi, I wanted to understand if usually end users utilize KQL db in fabric for storing historical data as well or only streaming data. What is usual retention period commonly followed for KQL db as the default is unlimited. Thanks in advance.Anomaly Detection
Hi, The walkthrough for real-time analysis in Fabric has the query below as an example of anomaly detection in taxi driver tips in New York: nyctaxitrips | lookup (Locations) on $left.PULocationID==$right.LocationID | where Borough == "Manhattan" | make-series s1 = avg(tip_amount) on tpep_pickup_datetime from datetime(2022-06-01) to datetime(2022-06-04) step 1h | extend anomalies = series_decompose_anomalies(s1) | render anomalychart with (anomalycolumns=anomalies) What bothers me is that the detection is made over an average, so we are not really detecting the anomaly, we are detecting the hour inside which the anomaly happened. The make-series only work with an aggregation function. Is it possible to use series_decompose_anomalies to detect individual anomaly values, instead of aggregated anomaly values? How could we use the make series without aggregation? The example contained in the description page of the function series_decompose_anomalies is about web traffic and makes complete sense, because there is no anomaly on an individual value, it only exists on an aggregation. But for taxi driver tips, there is one anomaly and we can't find it aggregating. Of course I could use separate queries over the hours pointed as anomaly to discover which is the anomaly value, but I was willing to find a solution to see the anomalies in a chart with a query. Am I wrong? Is my concept wrong? Kind Regards, Dennes
