SevOne Data Cloud
The Monitoring & AIOps Platform for Network Streaming Telemetry
- Delivers real-time and historical insights derived from network streaming telemetry and syslogs at cloud scale.
- Transforms raw network streaming telemetry data into a manageable flow of metrics, enabling analysis of the items that matters most.
- Enables easy visualization of streaming network telemetry and syslog metrics with persistent and real-time dashboards.
- Makes exploring data fast and easy with ad-hoc queries of real-time and historical metrics, with high-cardinality resource selection.
Unlimited Service-based Tagging
- Leverage default tagging based on the known data structure of the source device.
- Extend insights with flexible user-based tagging for business context.
- Utilize tagging system-wide: searches, aggregations, policy creation, event stream subscriptions, and more.
- Create customized groupings, aggregations and policies on-the-fly through dynamic association of metric and tag data stores.
Real-Time and On-Demand Policy-Based Analytics
- Run analytics continuously against real-time streams at ingestion, including aggregations, baselines and thresholds.
- Initiate on-demand, run-time analytics against queried data, including transformation, smoothing, bounds and seasonality-based analytics.
- Create policies quickly and easily based on historical metrics.
- Subscribe to tag-based based policy events and integrate with ITSM systems via webhooks.
Advanced Analytics with SevOne Data Cloud
Below is a sample of some of the analytics available with SevOne Data Cloud. A full list is available on the Data Cloud datasheet.
Exponential Standard Deviation
Show the expected range of many data types, with an emphasis on the range of recent data.
Group Standard Deviation
Show the expected range of the matched indicators at each point in time.
Show the variation among values in a data set.
Identify outliers at each point in time among a group of indicators.
Identify outliers at each point in time among a group of indicators, without any biases caused by extremely large or small values.
Help users judge whether the current data point is departing from its recent operating range.
Compute and display the expected data range with an emphasis on recent days or weeks.