Note
Access to this page requires authorization. You can try signing in or changing directories.
Access to this page requires authorization. You can try changing directories.
This reference architecture shows how to use Microsoft Fabric Real-Time Intelligence to ingest, enrich, analyze, and act on ISP telemetry at scale—processing more than 1 TB/hour of router logs alongside customer signals. By joining streaming events with operational context from Enterprise Resource Planning (ERP) systems (inventory, service plans, locations, and maintenance data), you can monitor network health in real time, investigate incidents faster, and predict emerging issues before they impact customers.
It also maps the end-to-end flow—from ingestion and enrichment to ML scoring and real-time dashboards, so you can adapt the pattern to your own network and operating model.
Architecture overview
The ISP analytics reference architecture uses Microsoft Fabric Real-Time Intelligence to create a unified platform that processes high-volume router logs and customer data streams, enriched with ERP context to enable intelligent network operations. You can implement the architecture with four main operational phases: Ingest and process, Analyze, transform and enrich, Train and score, and Visualize and activate.
The architecture includes the following key steps:
Ingest router logs - More than 1 TB/hour of router logs stream from customer endpoints and are captured in real time through Eventstreams.
Collect customer data - Customer information including device specifications, addresses, and billing plans is pushed via Message Queuing Telemetry Transport (MQTT) and collected by Eventstreams for enrichment.
Sync ERP metadata - Contextual metadata from ERP sources (inventory, network components, technician schedules) is synchronized to OneLake using Data Factory change data capture (CDC) to support data enrichment.
Enrich streaming data - Router logs are enriched in motion with customer details and operational context, creating analysis-ready datasets in real time.
Aggregate for analytics - Enriched data is aggregated in real time within Eventhouse, providing easy-to-query long-term views for network performance analysis.
Train and score ML models - Machine learning models are built and trained using Data Science, then deployed for real-time scoring to predict network issues and optimize performance.
Visualize network health - Real-Time Dashboards present live views of network usage with drill-down capabilities from system-wide metrics to individual router performance.
Generate BI reports - Power BI connects to Eventhouse via DirectQuery to deliver rich business intelligence reports on real-time and historical network data.
Activate alerts - Data Activator monitors router telemetry and generates real-time alerts on anomalies, threshold violations, and network issues.
Operational phases
Operational phases describe how data moves through the solution from ingestion to action. Each phase represents a distinct stage of data processing, and together these phases form a complete pipeline that transforms raw telemetry into actionable intelligence. The phases highlight the primary Fabric experiences involved, the type of processing performed, and the outcomes you can expect—starting with streaming ingestion and contextual enrichment, then progressing through analytics and machine learning scoring, and ending with dashboards and automated alerts that support day-to-day network operations.
Ingest and process
The Ingest and process phase focuses on reliably capturing high-volume streaming telemetry and shaping it into analysis-ready events. During this phase, you bring router logs and customer/device signals into Fabric in near real time, apply basic validation and normalization, and ensure the data is available for downstream enrichment, analytics, and alerting. This foundation is critical because the quality and completeness of ingested data directly affects the accuracy of all downstream processing.
More than 1 TB/hour of router logs from customer endpoints are ingested through Eventstreams (for example, via MQTT). This ingestion layer standardizes and routes events for downstream enrichment, analytics, and alerting. The following types of data are captured:
Router performance logs: These logs capture bandwidth utilization, latency metrics, and connection quality data that provide the foundation for network performance monitoring.
Network traffic patterns: Traffic data enables real-time usage analytics and provides insights that support capacity planning decisions.
Error logs and diagnostics: Diagnostic data captures network issues, outages, and performance degradations that require immediate attention or trend analysis.
Security events: Security telemetry includes intrusion attempts, suspicious traffic patterns, and network anomalies that might indicate threats.
Customer data (device, address, billing plan, and more) is pushed via MQTT and collected by Eventstreams. This customer context enables you to correlate network events with specific accounts and service levels:
Device information: Device records include hardware specifications, firmware versions, and configuration details that help diagnose customer-specific issues.
Customer profiles: Profile data encompasses service plans, billing information, and account status that determine service level expectations.
Location data: Geographic information supports service area analytics and helps identify regional patterns in network performance.
Service quality metrics: These metrics capture customer experience and satisfaction indicators that connect technical performance to business outcomes.
Contextual metadata (inventory, network components, technician schedules, and more) is synced from ERP systems to OneLake using Data Factory change data capture (CDC) to enrich streaming events. This operational context adds business meaning to raw telemetry:
Network inventory: Inventory data includes equipment specifications, physical locations, and maintenance schedules that support asset management.
Infrastructure topology: Topology information describes network architecture and connectivity mapping that helps operators understand dependencies.
Technician schedules: Schedule data supports field service optimization and resource allocation for maintenance activities.
Vendor information: Vendor records track equipment suppliers, maintenance contracts, and support agreements that affect service delivery.
Regulatory compliance: Compliance data captures service quality standards and reporting requirements that govern operations.
Analyze, transform, and enrich
In the Analyze, transform, and enrich phase, streaming data is transformed into analytics-ready intelligence as it moves through the system. Eventhouse enriches live router telemetry in motion with customer, device, and service context, producing curated datasets in real time. This processing standardizes, correlates, and aggregates high-volume events to support both immediate operational insight and long-term network analysis. The enriched data is also persisted to OneLake for historical analytics and compliance reporting, ensuring that you can analyze trends over time while maintaining the ability to respond to events as they occur.
Real-time data enrichment
Real-time data enrichment contextualizes router telemetry in Eventhouse by combining customer profiles, device details, geographic signals, and service quality metrics to produce analytics-ready datasets for operational insight. This enrichment happens as data flows through the system, adding context without introducing processing delays.
Customer context integration: Router logs are enriched with customer profiles, service plans, and billing information to connect technical events to specific customer accounts.
Device correlation: Network data is combined with device specifications and configuration details to enable device-specific analysis and troubleshooting.
Geographic enrichment: Traffic patterns are enhanced with location data and service area mapping to support regional performance analysis.
Service quality correlation: Performance metrics are combined with customer experience indicators to understand how technical issues affect service quality.
Enriched data is aggregated in real time to deliver easy-to-use, long-term views that support both operational monitoring, and strategic analysis. Advanced processing includes:
Network performance aggregation: The system computes bandwidth utilization, latency trends, and capacity metrics in real time to provide current performance visibility.
Customer usage patterns: Dynamic analysis reveals consumption behaviors and service utilization patterns that inform capacity planning and service design.
Geographic analytics: Regional performance analysis and service quality distribution help identify areas that need attention or investment.
Trend analysis: Historical pattern identification and predictive insights generation support proactive decision-making.
Processed data streams are persisted to OneLake tables, enabling comprehensive network intelligence that supports multiple use cases:
Long-term network performance analysis provides historical context for understanding trends and evaluating improvements.
Customer behavior pattern recognition helps you understand how customers use your services and identify opportunities for optimization.
Infrastructure optimization insights support data-driven decisions about network investments and capacity allocation.
Regulatory compliance reporting capabilities ensure you can meet documentation and audit requirements efficiently.
Train and score
The Train and score phase builds, trains, and deploys machine learning models that continuously evaluate streaming network data and score events in real time. By applying Microsoft Fabric Data Science capabilities, the architecture transforms enriched telemetry into predictive insights that help operators anticipate issues, optimize performance, and take proactive action before customer impact occurs. This phase is where your data becomes truly predictive, enabling you to move from reactive troubleshooting to proactive network management.
Predictive network analytics
Predictive network analytics applies machine learning to identify emerging operational risks, capacity constraints, and customer experience degradation by correlating historical trends with live telemetry. These models improve reliability, scalability, and customer satisfaction by catching problems early.
Network issue prediction: Machine learning models forecast potential outages and performance degradations by analyzing patterns that precede failures, giving operators time to intervene.
Capacity planning: Predictive analytics help you anticipate future bandwidth requirements and determine when to scale infrastructure by analyzing historical usage trends and growth patterns.
Customer churn prediction: Analysis of service quality metrics reveals their impact on customer retention, helping you prioritize improvements that affect customer loyalty.
Anomaly detection: Real-time identification of unusual network patterns and security threats enables rapid response to emerging issues.
Advanced machine learning capabilities
Advanced machine learning capabilities extend predictive analytics with sophisticated modeling techniques that optimize network operations and support long-term planning. These models learn from real-world outcomes to improve accuracy over time, enabling intelligent automation for performance optimization, service quality forecasting, and predictive maintenance decisions.
Performance optimization: Machine learning models identify opportunities for improved network efficiency and optimal resource allocation across your infrastructure.
Quality of service prediction: Forecasting models predict customer experience and satisfaction metrics, enabling proactive service improvements.
Maintenance scheduling: Predictive models determine optimal timing for equipment maintenance and replacement to minimize service disruption.
Traffic forecasting: Bandwidth demand prediction and capacity planning analytics help you prepare for future load before it arrives.
Visualize and activate
The Visualize and activate phase makes insights actionable for day-to-day network operations by combining real-time visualization, automated alerts, and self-service investigation tools. Together, these capabilities enable operators to monitor network health continuously, investigate issues quickly when they arise, and respond proactively to anomalies and service degradation. This phase represents the point where all your data processing efforts translate into operational value.
Real-time dashboards built with Real-Time Dashboard provide a live, continuously updated view of network usage and performance. Operators can move seamlessly from high-level monitoring to detailed, contextual drill-down, enabling faster diagnosis and informed operational decisions. The dashboards deliver the following capabilities:
Comprehensive network monitoring
Comprehensive network monitoring provides essential operational visibility into overall network health, usage, and service quality. This capability supports continuous monitoring across regions, customer segments, and infrastructure components, giving operators a complete picture of network status at any moment.
Network overview: The network overview presents a high-level view of overall network performance and health status that helps operators quickly assess system-wide conditions.
Regional analysis: Regional analysis shows the geographic distribution of network usage and service quality, helping you identify location-specific patterns and issues.
Customer segment views: Segment views display service utilization patterns across different customer tiers, enabling you to understand how different customer groups experience your network.
Equipment monitoring: Equipment monitoring provides real-time status of routers, switches, and network infrastructure to support proactive maintenance and rapid issue identification.
Multi-granularity network analysis
Multi-granularity network analysis enables progressive drill-down from aggregated metrics to customer, region, and device-level telemetry. This capability supports deep analysis without losing real-time context, so operators can investigate issues at any level of detail.
System-wide metrics: System-wide metrics display overall bandwidth utilization, latency, and performance indicators that reflect the health of your entire network.
Regional drill-down: Regional drill-down provides geographic performance analysis with regional service quality metrics that reveal location-specific performance characteristics.
Customer-specific analysis: Customer-specific analysis shows individual customer usage patterns and service experience to support account-level troubleshooting and service optimization.
Device-level monitoring: Device-level monitoring offers detailed router and equipment performance tracking that enables precise diagnosis of hardware-related issues.
Real-time alerts generated using Activator enable proactive operational response by identifying network anomalies and threshold violations as they occur. These alerts ensure that the right teams are notified immediately when action is required.
Automated network alerting
Automated network alerting routes critical operational signals to the right teams with low-latency notifications. This capability helps operators mitigate issues before they affect service availability or customer experience, reducing the time between issue detection and response.
Performance threshold alerts: Performance threshold alerts send notifications when bandwidth, latency, or capacity metrics violate defined limits, enabling rapid response to performance issues.
Anomaly detection alerts: Anomaly detection alerts provide immediate notification for unusual network patterns and security threats that might require investigation.
Equipment failure notifications: Equipment failure notifications deliver real-time alerts for router malfunctions and infrastructure issues that could affect service delivery.
Service quality alerts: Service quality alerts notify teams of customer experience degradation and SLA violations so they can prioritize customer-impacting issues.
DirectQuery from Power BI to Eventhouse complements real-time dashboards with rich analytical reporting on live and historical network data. This integration supports trend analysis, optimization initiatives, and executive reporting needs.
Strategic network analytics
Strategic network analytics provides executive and operational insights by combining real-time and historical network data. These reports support performance management, capacity planning, and compliance reporting, connecting operational data to business decisions.
Network performance reporting: Network performance reporting provides analysis of service delivery effectiveness and infrastructure efficiency to support operational improvement initiatives.
Customer analytics: Customer analytics reveal usage patterns, service satisfaction trends, and revenue optimization insights that inform business strategy.
Regulatory compliance: Regulatory compliance capabilities support service quality reporting and regulatory requirement documentation to meet industry obligations.
Ad-hoc queries using KQL enable deep investigations in minutes instead of hours. This self-service capability empowers operators to explore data freely and answer questions as they arise.
Advanced network investigation
Advanced network investigation enables rapid troubleshooting and root-cause analysis across network and customer signals. These capabilities reduce the time required to diagnose and resolve complex issues.
Performance troubleshooting: Performance troubleshooting provides rapid analysis of network issues and root cause identification to accelerate problem resolution.
Customer experience analysis: Customer experience analysis enables investigation of service quality and satisfaction factors that affect customer perception.
Security incident response: Security incident response supports forensic analysis of network security events and threats to protect your infrastructure and customers.
Operational optimization: Operational optimization analysis identifies opportunities for network efficiency improvements and resource allocation enhancements.
Using Copilot, network operations teams can ask natural language questions to accelerate investigations and simplify analysis. This conversational interface reduces the learning curve for advanced data exploration and helps teams find answers faster.
Technical benefits and outcomes
This architecture delivers measurable operational and business outcomes by combining real-time telemetry, contextual enrichment, and automated activation. When you implement this pattern end to end, you can expect improvements across four key areas: network intelligence, automated operations, advanced analytics, and cost optimization. The following sections describe these benefits in detail.
Network intelligence and optimization
Improve deeper situational awareness and make smarter optimization decisions by correlating high-volume network telemetry with customer profiles and operational context. This unified view helps you detect issues faster, understand their impact, and prioritize responses based on business value.
Real-time network monitoring: The architecture enables you to monitor more than 1 TB/hour of router logs continuously, giving you near real-time visibility into network performance across your entire infrastructure.
Predictive network analytics: Machine learning models analyze historical and live data to predict network issues before they occur, allowing you to optimize infrastructure performance proactively.
Unified network platform: By integrating router logs with customer data and ERP context, the platform provides comprehensive network intelligence that connects technical metrics to business outcomes.
Multi-granularity analysis: Real-time dashboards support drill-down from system-wide overviews to individual device details, as described in the Visualize, and activate phase.
Automated network operations
Reduce manual effort and accelerate incident response by converting network signals into automated alerts and workflows. This automation frees up operations teams to focus on strategic improvements rather than routine monitoring tasks.
Intelligent network alerting: The system sends real-time notifications when performance thresholds are exceeded, anomalies are detected, or equipment failures occur, ensuring that the right teams are informed immediately.
Automated response workflows: Based on predefined conditions, the architecture can trigger escalations, schedule maintenance activities, and send customer notifications without manual intervention.
Proactive network management: Predictive models support capacity planning and preventive maintenance by identifying potential issues before they affect service availability.
Dynamic resource allocation: The platform enables real-time adjustments to network capacity and service quality parameters, helping you respond to changing demand patterns automatically.
Advanced analytics and business intelligence
Empower operations and business teams with self-service analytics capabilities, including real-time business intelligence, ad-hoc investigation tools, and conversational querying. These capabilities help teams find answers faster and make more informed decisions.
Real-time network analytics: By correlating router performance metrics with customer experience data, the architecture helps you optimize service delivery and identify improvement opportunities.
Cross-service intelligence: Power BI reports analyze network infrastructure performance alongside customer service metrics, providing a holistic view of operational effectiveness.
Natural language processing: Operations teams can use conversational AI combined with KQL to explore complex network scenarios using natural language questions, reducing the learning curve for advanced analysis.
Predictive and historical analysis: The platform combines real-time event streams with historical patterns to improve network management through trend analysis and predictive insights.
Cost optimization and operational efficiency
Lower operational costs and improve efficiency by predicting issues earlier and prioritizing actions based on data-driven insights. This approach shifts spending from reactive firefighting to proactive optimization.
Predictive cost management: Machine learning-driven issue prediction helps reduce unplanned outages and maintenance costs by identifying problems before they escalate.
Infrastructure efficiency: Predictive analytics enable you to maximize network utilization while minimizing service disruptions, improving the return on infrastructure investments.
Operations optimization: Automated analytics and investigation tools improve network management effectiveness by reducing the time required to diagnose and resolve issues.
Strategic decision support: Data-driven insights support informed decisions for infrastructure investment, capacity expansion, and service optimization initiatives.
Implementation considerations
Use the following considerations to adapt the reference architecture to your specific environment and operational requirements. These guidelines address practical requirements across four critical areas: data architecture, security and compliance, system integration, and ongoing monitoring. By addressing these considerations early in your planning process, you can implement the solution reliably at scale and avoid common pitfalls.
Data architecture requirements
A well-designed data architecture ensures that your system can handle high-volume telemetry while maintaining data quality and meeting latency requirements. Plan your ingestion, processing, and storage strategy carefully to support both real-time operations and long-term analytics needs.
High-throughput ingestion: Your system should be designed to process more than 1 TB/hour of router logs reliably, with more burst capacity to handle peak network usage periods without data loss or degraded performance.
Real-time processing: Critical network alerts require immediate response times, while performance monitoring needs subsecond latency to enable operators to detect and respond to network issues as they occur.
Data quality and validation: You should implement real-time validation to ensure router log accuracy, customer data integrity, and correct network performance calculations, including automatic error correction mechanisms that maintain data quality without manual intervention.
Scalability planning: The architecture should be designed to accommodate growing network infrastructure, an expanding customer base, and increasing data volumes without requiring significant rearchitecture.
Storage requirements: Plan your storage strategy to support real-time logs, historical performance records, and customer analytics, with retention policies that balance operational needs against storage costs.
Network systems integration: Your data architecture should include well-defined integration patterns for routers, MQTT protocols, and network management systems to ensure consistent data flow.
Security and compliance
Protecting customer and operational data is essential for maintaining trust and meeting regulatory obligations. Apply governance controls that address telecommunications industry requirements while enabling the operational agility your teams need to respond to network issues quickly.
Access controls: You should implement role-based access control that aligns with your operational roles, require multifactor authentication for all users, and use privileged access management for sensitive infrastructure functions to limit exposure.
Audit trails: Comprehensive audit logging is essential for compliance, and your implementation should capture network monitoring activities, customer data access, and system operations in logs that support regulatory reporting.
Data privacy: Your implementation must ensure compliance with telecommunications regulations, customer privacy requirements, and data protection standards that apply to both network telemetry and customer information.
Integration points
Successful implementation depends on well-defined connections between the Fabric platform and your existing network systems and business applications. These integrations ensure that data and context remain synchronized across the entire solution, enabling the contextual enrichment that makes this architecture valuable.
Network infrastructure: Your implementation should integrate directly with routers, switches, and other network equipment to collect performance logs and telemetry in real time without disrupting network operations.
MQTT protocols: Customer devices and IoT endpoints communicate via MQTT, and your Eventstreams configuration should be optimized to handle the message patterns and volumes these devices generate.
ERP systems: Integration with your ERP systems provides essential context for inventory management, technician scheduling, and network asset data that enriches streaming telemetry with business meaning.
Customer management: Connections to billing systems, customer service platforms, and CRM systems enable you to correlate network performance with customer experience and business impact.
External data sources: Your architecture should include integration points for external APIs that provide vendor system data, regulatory database information, and third-party network monitoring insights.
Monitoring and observability
Continuous monitoring of both platform health and data correctness helps you detect ingestion failures, processing bottlenecks, and cost drivers before they affect operations. A comprehensive observability strategy ensures that you can maintain system reliability while optimizing costs over time.
Operational monitoring
Operational monitoring tracks service health and end-to-end performance across ingestion, processing, and alerting components. This visibility helps your team identify and resolve issues before they affect downstream analytics or alerting.
System health dashboards: You should monitor router log ingestion rates, Eventhouse processing performance, and Activator alert delivery continuously, with automated alerts that notify your team when system anomalies occur.
Data quality monitoring: Continuous validation of incoming network data helps you identify communication failures, invalid performance indicators, or corrupted router information before bad data affects your analytics.
Performance metrics: Track ingestion latency, machine learning model prediction accuracy, and dashboard response times against your defined SLAs to ensure the system meets operational requirements.
Cost optimization
Cost optimization requires balancing performance requirements against spending through careful capacity planning, appropriate retention policies, and lifecycle management aligned to your regulatory and operational needs.
Capacity management: You should right-size your Fabric capacity based on actual data volumes and analytics complexity, configure autoscaling for peak usage periods, and optimize spending during low-activity windows.
Data lifecycle management: Archiving older network data to lower-cost storage tiers, applying retention policies that align with regulatory requirements, and removing nonessential operational data help control storage costs over time.
Network operations optimization: By correlating network performance metrics with operational costs, you can identify opportunities to minimize maintenance expenses while maximizing infrastructure efficiency.
Next steps
Follow this phased approach to implement and scale the reference architecture in your environment. The recommended path begins with a focused pilot that validates core capabilities, then progresses through operational validation before expanding to full-scale deployment. This incremental approach helps you identify integration challenges early, build team expertise, and demonstrate value before committing to enterprise-wide rollout.
Getting started
The initial implementation phase establishes the foundational Fabric experiences and validates the end-to-end pipeline with a limited scope. By starting small and proving the architecture works in your environment, you can build confidence and refine your approach before scaling across your full network infrastructure.
Phase 1: Foundation setup
During the foundation phase, you establish the core platform components and integration patterns that support the entire solution.
Begin by reviewing the Microsoft Fabric Real-Time Intelligence capabilities and estimating capacity requirements based on your network size, expected data volumes, and analytics complexity.
Plan your Eventstreams integration strategy to define how you'll ingest router logs and customer data via MQTT, including message formats and routing rules.
Design your real-time analytics implementation in Eventhouse to ensure the system can process network events with the immediate response times your operations require.
Configure OneLake to store ERP contextual metadata and historical network analytics, establishing retention policies that balance operational needs with storage costs.
Phase 2: Pilot implementation
The pilot phase validates your architecture with real data from a subset of your network, helping you identify and resolve integration issues before broader deployment.
Start with a representative subset of network infrastructure and customer segments that allows you to validate the architecture and measure integration performance under realistic conditions.
Implement the core data flows for network monitoring, performance analytics, and basic alerting to establish the foundation for more advanced capabilities.
Establish integrations with network equipment and ERP systems to validate that end-to-end contextualization works correctly and produces meaningful enriched data.
Deploy a Real-Time Dashboard that provides network monitoring, drill-down analysis, and performance assessment capabilities to demonstrate value to stakeholders.
Phase 3: Operational validation
The operational validation phase confirms that your implementation meets performance requirements and prepares your team to operate the solution effectively.
Test system performance during peak network usage periods and high-volume data scenarios to ensure the architecture can handle production workloads reliably.
Validate your Activator rules to confirm that network alerts and anomaly detection trigger correctly and reach the appropriate teams.
Review compliance with telecommunications regulations and service quality standards to ensure your implementation meets all regulatory obligations.
Train network operations teams on dashboard usage, alert management, and KQL investigations so they can use the new capabilities effectively from day one.
Advanced implementation
Once your pilot is stable and your team is comfortable with the core capabilities, you can add intelligent automation and scale the solution across your entire network. This phase introduces machine learning, advanced alerting, and enterprise-grade reporting capabilities.
Intelligent automation and AI**
The intelligent automation phase adds predictive capabilities and automated responses that reduce manual effort and accelerate issue resolution.
Set up advanced Data Science capabilities to build, train, and score network prediction models that optimize performance and anticipate issues before they affect customers.
Implement Activator to automate operational responses such as predictive maintenance scheduling, dynamic capacity adjustments, and alert-driven workflows that reduce manual intervention.
Deploy Copilot to enable natural language analytics that allow operations teams to investigate issues and explore data using conversational queries.
Build intelligent network management workflows that provide real-time decision support based on performance patterns, customer behavior analysis, and predictive analytics insights.
Enterprise-scale deployment
The enterprise deployment phase extends the solution across your full infrastructure with comprehensive monitoring and executive reporting capabilities.
Scale the solution to cover full ISP operations with comprehensive monitoring and centralized visibility across all infrastructure components and customer segments.
Implement advanced analytics capabilities for cross-service optimization, capacity management, and performance effectiveness analysis that drive continuous improvement.
Create executive dashboards using Power BI DirectQuery capabilities and Real-Time Dashboard for operational monitoring, executive reporting, and regulatory compliance documentation.
Develop enterprise-grade machine learning models for network prediction, customer experience optimization, and infrastructure investment planning that support long-term strategic decisions.