Skip to content Business Need
The client, a prominent government organization, sought a dedicated solution to automate the collection of logs from a wide array of sources within their infrastructure, including servers, virtual machines, containers, networks, middleware, applications, and databases. The objective was to centralize this data for comprehensive analysis. Additionally, the client aimed to implement cutting-edge AI and ML tools to autonomously learn data patterns and proactively detect real-time issues across their entire infrastructure. They required robust visualization tools to identify data patterns within logs and a dashboard to isolate and analyze error, debug, and audit logs.
Business Challenge
Prior to implementing the solution, the client encountered several challenges. The use of various dashboards from multiple monitoring tools was time-consuming and inefficient. The daily influx of data was significant, leading to delays in issue identification due to the need to correlate monitoring data points, including events and logs.
Distinguishing false alarms from different systems posed a challenge. Managing complex alerting and triggering predefined actions with third-party integrations added complexity. Moreover, there was a pressing need to integrate monitoring tools with the ticketing system for seamless issue resolution.
Business Solution
- High Availability with Elastic Platform Integration: Leveraged the Elastic platform with Fluentd and Kafka to ensure resilient, high-availability infrastructure.
- Centralized Log Management: Enabled efficient search and analysis across petabytes of logs from a single, unified platform.
- Scalable Hybrid Infrastructure Monitoring: Simplified and scaled infrastructure monitoring across complex hybrid environments.
- Elimination of Tool Silos: Streamlined operations by removing tool silos, fostering seamless interoperability.
- Enhanced Visibility Across Hybrid Environments: Provided end-to-end visibility, improving oversight and control across all platforms.
- AI-Driven Insights with Machine Learning and Automation: Incorporated machine learning and automation to deliver proactive, actionable insights.
- Continuous Digital Experience Monitoring: Enabled round-the-clock monitoring to ensure a seamless user experience.
Project Differentiator
The solution provided by NuSummit offered several differentiators, including:
- Maximized Uptime: Ensured high availability and reliability, minimizing service interruptions.
- Instant Data Reporting: Enabled real-time generation of critical data reports for faster decision-making.
- Comprehensive Performance Analysis: Provided deep insights into performance constraints to drive continuous improvement.
- Automated Pattern Recognition with Machine Learning: Leveraged machine learning to automatically identify data patterns, enhancing predictive capabilities.
Tech Stack
- Elasticsearch
- Beats
- Logstash
- Kibana
Business Impact
The project delivered significant benefits, including:
- High-Availability Multi-Cluster Environment: Enabled seamless operations with automatic failover to ensure continuous service.
- Integration of 400+ Device and Application Logs: Centralized logging with customized parsers for over 400 unique sources, enhancing data visibility.
- Live Capacity Reporting and Forecasting: Provided real-time capacity insights and predictive forecasts across all infrastructure components.
- Detailed Performance Analytics: Delivered in-depth analytics to quickly identify and address bottlenecks at any component level.
- Real-Time Data Pattern Recognition with Machine Learning: Utilized machine learning to detect patterns instantly, supporting proactive decision-making.
