Lilly Tech Systems
Network Data Analytics
Transform raw network telemetry into actionable insights. Learn to collect data from diverse sources, analyze traffic flows, build powerful visualizations, and apply predictive analytics to anticipate network issues before they impact users.
What You'll Learn
Develop expertise in network data analytics from collection through prediction.
Data Source Mastery
Understand SNMP, syslog, NetFlow/IPFIX, streaming telemetry, and API-based data collection strategies.
Flow Analysis
Analyze network flows to identify top talkers, detect anomalies, and understand application behavior patterns.
Data Visualization
Create dashboards and visualizations using Grafana, Matplotlib, and D3.js for network performance data.
Predictive Analytics
Build time-series models to forecast bandwidth, predict failures, and optimize capacity planning.
Course Lessons
Follow the lessons in order or jump to any topic you need.
1. Introduction
Overview of network data analytics: why data-driven networking matters, the analytics pipeline, and key tools in the ecosystem.
2. Data Sources
Deep dive into network data sources: SNMP polling, syslog messages, NetFlow/IPFIX, streaming telemetry, and REST APIs.
3. Flow Analysis
Techniques for analyzing network flows: top-N analysis, traffic profiling, baseline establishment, and anomaly detection.
4. Visualization
Building effective network dashboards with Grafana, creating custom visualizations, and presenting data to stakeholders.
5. Predictive Analytics
Time-series forecasting, capacity prediction, failure probability modeling, and proactive alerting with ML models.
6. Best Practices
Scaling analytics pipelines, data retention strategies, real-time vs. batch processing, and building a data-driven network team.
Prerequisites
- Basic networking fundamentals (TCP/IP, routing, switching)
- Familiarity with network monitoring tools (SNMP, syslog)
- Basic Python or data analysis experience (helpful)
- Understanding of basic statistics concepts