To analyze IoT-, streaming- and log-data or data that is based on the timeseries data you need database optimized for that. KQL database is just that. With Kusto as a query language you can analyze efficiently data from few gigabytes to petabytes.
Kusto Query Language is a powerful tool to explore your data and discover patterns, identify anomalies and outliers, create statistical modeling, and more. The query uses schema entities that are organized in a hierarchy similar to SQLs: databases, tables, and columns.
If you need to transform and save streaming data you can use Event Streams for that. With Event Streams it is easy to land data to open Delta Parquet format and also to KQL-database. From timeseries optimized Kusto database you can query aggregated data from petabytes of data in milliseconds.
KQL scales per query allowing you to run both large and small queries efficiently in your cluster. Builtin visualization cababilities allow you to see data in easy to understand format and for even richer visuals integration to Power BI is easy.
Qumio has worked with many manufacturing companies to analyze their production data and detect variation or anomalies from the data. KQL databases are important part of your analytical platform if you have time series data.
Data Activator is an upcoming feature to Microsoft Fabric. It makes it possible to create actions based on the data you have. Data can be coming from any workload. Actions make it possible to send email, teams messages, other short messages, call external workflows and many more. It’s like your automated reactive real time analytics solution.