Network Traffic Analysis at a financial institution with 788 branches for 3500 interfaces with NetFlow Analyzer
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A private bank with 788 branches sought to monitor 3,500 network interfaces and faced challenges in viewing real-time data and managing bandwidth usage. The NetFlow Analyzer provided solutions such as flow-based traffic analysis, centralized dashboards, and real-time statistics, significantly enhancing visibility and troubleshooting. This resulted in optimized resource usage, reduced downtime, and improved delivery of critical applications.
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Network Traffic Analysis at a financial institution with 788 branches for 3500 interfaces with NetFlow Analyzer
- 1. Reference Customer Implementation A Leading Bank with 788 branches Monitors 3500 Interfaces with NetFlow Analyzer
- 2. Customer Details • Private bank with 788 branches (Alabama, Florida, Georgia, Indiana, Kentucky, Louisiana, Mississippi, Maryland, New York, Ohio, Pennsylvania, Texas and more) • Interfaces to be monitored – 3500 interfaces distributed at different locations • Cost of downtime – $2 Million/hour
- 3. Challenges before using NetFlow Analyzer
- 4. Real-time view of vital statistics
- 5. Challenges & Problems • What are the resource-intensive applications? • How do I view my distributed network from one point? • How do I monitor which applications use maximum bandwidth?
- 6. Customer’s network Site A Site B Site C Central Server Illustration not to scale
- 8. • Flow-based traffic analysis • Real-time traffic reports • Top Applications – Identifying resource-intensive applications • Probe-Server architecture for distributed monitoring • Threshold-based alerts NetFlow Analyzer’s capabilities
- 9. How NetFlow Analyzer helped the institution
- 10. • Identifying Top Applications • Application-wise bandwidth usage • Monitoring distributed networks • Top conversations in the network • Getting real-time traffic statistics • Centralized configurable dashboard How NetFlow Analyzer helped
- 11. Business value • Single console view • Better visibility • Faster troubleshooting of sluggishness • Minimum downtime • Optimized usage of resources • Delivery of critical applications