Comprehensive Data Pipeline for Processing Syslogs and Fault Data Across Jio's Pan-India Cisco Network Infrastructure
To design and implement a comprehensive data pipeline for processing syslogs and fault data across Jio's pan-India Cisco network devices, enabling real-time fault detection, analysis, and proactive network maintenance through advanced analytics and alerting capabilities.
The Syslogs Fault Analytics project established a robust, enterprise-scale data processing system for Jio's pan-India Cisco network infrastructure. The system processes millions of syslog entries daily, providing comprehensive fault analysis, real-time monitoring, and proactive alerting capabilities to ensure optimal network performance and reliability.
Advanced syslog parsing, data cleaning, masking, and structuring workflows to transform raw network logs into actionable insights across thousands of Cisco devices.
Scalable Kafka producer/consumer streams enabling high-throughput, real-time log ingestion and processing from distributed network infrastructure.
Intelligent fault detection algorithms with near real-time alerting and notification system to improve network fault response and minimize downtime.
Sophisticated data aggregation mechanisms to consolidate fault events and provide comprehensive analytics for network performance optimization.
Designed the complete end-to-end data pipeline architecture for processing syslogs and fault data from thousands of Cisco network devices across Jio's pan-India infrastructure.
Developed comprehensive syslog parsing, data cleaning, masking, and structuring workflows to ensure high-quality data processing and compliance with security requirements.
Setup a 17 node Kafka cluster, built and optimized Kafka producer/consumer streams for scalable, real-time log ingestion and processing, handling high-volume network data with minimal latency.
Implemented sophisticated data enrichment workflows and aggregation mechanisms to consolidate fault events and provide meaningful insights for network operations.
Developed a near real-time alerting and notification system to improve network fault detection and response times, enabling proactive network maintenance.
Real-time syslog processing and intelligent fault detection significantly reduced mean time to identify network issues.
Near real-time alerting system enabled faster response to network faults and proactive maintenance scheduling.
System successfully processes over 2 TB's of size syslogs entries daily from pan-India network infrastructure.