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#Microwind 3.5 demo windows 10#
#Microwind 3.5 demo series#

#Microwind 3.5 demo series#

Once a set of time series is created, Azure Data Explorer supports a growing list of functions to process and analyze them which can be found in the time series documentation. In this section, we'll perform typical series processing functions.

#Microwind 3.5 demo windows 10#

We can create a separate time series: Windows 10 (red), 7 (blue) and 8.1 (green) for each OS version as seen in the graph: In the table above, we have three partitions. We use render timechart for visualization.

The actual time series data structure is a numeric array of the aggregated value per each time bin.

Alternatively use series_fill_const(), series_fill_forward(), series_fill_backward() and series_fill_linear() for changes

default=0: specify fill method for missing bins to create regular time series.

range(min_t, max_t, 1h): time series is created in 1-hour bins in the time range (oldest and newest timestamps of table records).

Use the make-series operator to create a set of three time series, where:.

| make-series num=count() default=0 on TimeStamp in range(min_t, max_t, 1h) by OsVer Let max_t = toscalar(demo_make_series1 | summarize max(TimeStamp)) let min_t = toscalar(demo_make_series1 | summarize min(TimeStamp)) Since there are no metrics, we can only build a set of time series representing the traffic count itself, partitioned by OS using the following query: The resulting table contains a timestamp column, three contextual dimensions columns, and no metrics: TimeStamp Use the command below to sample 10 records: The input table demo_make_series1 contains 600K records of arbitrary web service traffic.

The goal is to create thousands of time series per partition at regular time intervals. The dimensions are used to partition the data.

The table usually contains a timestamp column, contextual dimensions, and optional metrics. The first step in time series analysis is to partition and transform the original telemetry table to a set of time series. In this section, we'll create a large set of regular time series simply and intuitively using the make-series operator, and fill-in missing values as needed. In this topic, learn how Azure Data Explorer is used to create and analyze thousands of time series in seconds, enabling near real-time monitoring solutions and workflows. Analysis is done on time series of selected metrics to find a deviation in the pattern compared to its typical baseline pattern.ĪDX contains native support for creation, manipulation, and analysis of multiple time series. This data can be analyzed for various insights such as monitoring service health, physical production processes, and usage trends. Azure Data Explorer (ADX) performs on-going collection of telemetry data from cloud services or IoT devices.