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Case Studies

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Optimizing railway infrastructure with real-time IoT solutions

Optimizing railway infrastructure with real-time IoT solutions

Optimizing railway infrastructure with real-time IoT solutions

How Fractal built a scalable, automated framework for railway networks

How Fractal built a scalable, automated framework for railway networks

Automated framework on Azure

Real-time issue alerts

Retry mechanism for failures

Resilient operations

The challenge

Enhancing railway infrastructure with real-time IoT solutions

One of the largest railway network operators needed a real-time, IoT-enabled solution to efficiently manage its complex infrastructure. Fractal developed individual components and a real-time response system on the Azure cloud platform using the Databricks framework.

Key challenges

  • Ensuring instant detection of infrastructure issues

  • Connecting IoT data with existing railway systems

  • Handling vast amounts of data across the network

The solution

Smart infrastructure management with real-time analytics

Data and security

Built ingestion, storage, and analytics components

Secured networking on Azure

Enabled real-time data processing

Real-time insights

Live status reporting and analysis

Integrated drill-down analytics

Instant support for business systems

Implementation approach

1

Data processing and integration

  • Streamlined data ingestion and access

  • Integrated IoT sources with Azure

  • Added real-time metadata for insights

2

Real-time monitoring

  • Enabled real-time tracking and alerts

  • Supported proactive responses

  • Automated issue detection

3

Scalability and reliability

  • Built for high-volume processing

  • Ensured resilience with failover

  • Optimized uptime and performance

The impact

Seamless automation and real-time resilience

Automation and efficiency

  • Fully automated on Azure

  • Reduced manual effort

  • Boosted operational efficiency

Real-time reliability

  • Instant alerts and issue detection

  • Auto-retry for failures

  • Minimized downtime

Scalability and resilience

  • Handled high-volume data

  • Ensured seamless performance

  • Strengthened system reliability

Looking ahead

Enhanced AI integration

  • Leverage AI for predictive maintenance

Expanded scalability

  • Optimize for larger data volumes and regions

Continuous improvement

  • Refine automation and real-time insights