Key Highlights
Motadata ObserveOps ingests flow data from NetFlow, sFlow, jFlow, and IPFIX, segmenting traffic by application, geography, and user to reveal bandwidth patterns, QoS effectiveness, and anomalies.
Collect traffic data from all devices using each major flow protocol.
NetFlow v5, v9, and IPFIX collection from Cisco, Juniper, and standards-compliant devices.
sFlow and jFlow support for environments using sampling-based flow export.
Flexible collector configuration supporting multi-vendor flow export simultaneously.
Flow normalization of protocol variants into a unified traffic analysis model.
See how bandwidth is consumed and whether QoS policies deliver expected results.
Live bandwidth utilization per interface, site, and network segment.
Top-N talker and listener identification by volume, session count, and protocol.
Pinpoints the devices and applications consuming the most bandwidth.
QoS class monitoring of traffic classification, queuing, and forwarding behavior.
Congestion event detection identifying when and where bandwidth saturation occurs.
Break traffic into the dimensions that enable meaningful analysis and decisions.
Application-layer breakdown identifying which applications consume the most bandwidth.
Port and protocol segmentation for security investigation and capacity planning.
Geographic source and destination analysis for multi-site and cloud-connected environments.
Cloud service traffic identification showing volumes to major SaaS and cloud providers.
Identify traffic behavior that deviates from established patterns.
Machine learning baselines trained on historical traffic patterns per interface and segment.
Anomalous volume detection surfacing sudden traffic spikes and drops.
Protocol anomaly detection identifying unusual usage or unexpected flow characteristics.
Security-relevant pattern detection for data exfiltration and lateral movement indicators.
See how traffic patterns evolve and plan for future capacity.
Long-term traffic volume trending for capacity planning and bandwidth forecasting.
Peak utilization identification for peak-period planning and QoS policy tuning.
Seasonal pattern analysis connecting traffic volume to business calendar events.
Traffic growth rate calculation enabling data-driven bandwidth procurement decisions.
Deliver traffic intelligence in the format each operational and planning function needs.
Security investigation dashboards with source, destination, protocol, and volume context.
Capacity planning dashboards with utilization trends, peak analysis, and forecast projections.
Application performance dashboards showing traffic volumes per application and segment.
Executive dashboards with bandwidth cost, utilization efficiency, and capacity risk summaries.
Intelligence
Raw flow records show that traffic moved from one IP to another, but they do not explain which application generated the flow, how much bandwidth it consumed, or whether it met QoS policy. Lacking application-layer segmentation, a WAN saturation event could be a backup job, a video conference spike, a new SaaS application, or data exfiltration, and all four look identical.
Intelligent Traffic Analysis provides the segmentation, baselining, and anomaly detection that converts flow data into operational intelligence. Bandwidth problems are diagnosed, capacity decisions validated, and security incidents identified with full application-level and historical context.
How It Works
Collect flow records via NetFlow, sFlow, jFlow, and IPFIX from device flow exporters.
Normalize flow data from protocol variants into Motastore's unified traffic model.
Apply application identification, geographic enrichment, and cloud service classification.
Train baseline models on historical traffic patterns per interface, application, and segment.
Run anomaly detection against incoming flow data for volume, protocol, and pattern deviations.
Render segmented traffic analysis through decision-ready operational dashboards.
Each byte understood, in context, in sequence, and compared to what came before.
Role-Based Value
Make capacity investment decisions on actual traffic consumption data, not estimates.
Make capacity investment decisions on actual traffic consumption data, not estimates.
See what consumes enterprise bandwidth and whether expensive WAN links are used efficiently.
See what consumes enterprise bandwidth and whether expensive WAN links are used efficiently.
Identify the application or source behind a bandwidth saturation event within seconds.
Identify the application or source behind a bandwidth saturation event within seconds.
Use historical traffic trending and application-level growth data to design capacity upgrades.
Use historical traffic trending and application-level growth data to design capacity upgrades.
From Visibility to Control
45% faster root-cause identification through contextual traffic segmentation during bandwidth events.
Anomaly-based security detection surfacing behavioral deviations invisible to threshold alerting.
Application-level bandwidth accounting enabling evidence-based QoS policy decisions.
Capacity forecasts derived from measured traffic growth trends, not estimates.
Multi-protocol flow support ensuring coverage across all vendors and segments.
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