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Border Network Gateway KPIs

Apache Kafka PySpark Real-time Telemetry Python

Real-time Telemetry System for Network Performance Monitoring at Jio Broadband

Project Objective

To design and implement a comprehensive real-time monitoring system for Border Network Gateway (BNG) Key Performance Indicators (KPIs) at Jio Broadband, enabling proactive network management and performance optimization through telemetry-driven insights.

  • Build real-time telemetry data processing pipeline for network KPIs
  • Implement high-volume data ingestion and processing capabilities
  • Create automated alerting system for network performance anomalies
  • Develop comprehensive monitoring dashboards for network operations

Project Description

The Border Network Gateway KPIs project established a robust telemetry-driven monitoring system for Jio Broadband's network infrastructure. The system processes millions of telemetry data points in real-time to provide comprehensive insights into network performance, enabling proactive maintenance and optimization.

Real-time Telemetry Processing

High-throughput data pipeline processing network telemetry data from thousands of BNG devices across the Jio network infrastructure.

Performance Monitoring

Comprehensive KPI tracking including bandwidth utilization, latency metrics, packet loss rates, and connection statistics.

Intelligent Alert System

Machine learning-based anomaly detection and automated alerting for network performance issues and potential failures.

Analytics Dashboard

Real-time visualization dashboards providing network operators with actionable insights and historical trend analysis.

Technical Stack

Stream Processing

Apache Kafka Apache Spark Streaming PySpark

Data Processing & Analytics

Apache Spark Pandas NumPy scikit-learn

Storage & Database

HDFS Mysql MongoDB Redis

Monitoring & Visualization

Logstash Syslogs ELK Stack

My Contribution

System Architecture Design

Designed the complete telemetry processing architecture including data ingestion, stream processing, storage, and visualization layers for handling high-volume network data.

Real-time Data Pipeline Development

Built robust Kafka-based streaming pipelines with python consumers for real-time processing of network telemetry data from thousands of BNG devices.

KPI Calculation Engine

Developed complex algorithms for calculating network performance KPIs including bandwidth utilization, latency percentiles, and availability metrics.

Anomaly Detection System

Implemented anomaly detection algorithms to identify network performance issues and potential failures before they impact users.

Monitoring Dashboard Development

Created comprehensive Grafana dashboards for real-time network monitoring, historical analysis, and operational insights for network engineering teams.

Business Outcome

Faster Issue Detection

Real-time monitoring and alerting significantly reduced mean time to detection for network performance issues.

Reduced Network Downtime

Proactive monitoring and predictive alerts helped prevent network outages and minimize service disruptions.

System Availability

Robust architecture ensured high availability of the monitoring system even during peak network traffic periods.

Key Business Impacts:

  • Improved Network Reliability: Proactive monitoring and early warning systems significantly improved overall network stability and user experience
  • Operational Efficiency: Automated monitoring and alerting reduced manual oversight requirements and enabled faster response times
  • Cost Optimization: Predictive maintenance capabilities helped optimize network resource utilization and reduce operational costs
  • Enhanced Decision Making: Real-time insights and historical analytics enabled data-driven network planning and capacity management
  • Scalable Infrastructure: System designed to handle growing network infrastructure and increasing data volumes without performance degradation