Challenges
The company needed to modernize its technology stack to enhance real-time data processing, scalability, resilience, and transparency. Legacy systems struggled to process IoT data efficiently, hindering real-time decision-making. As data volumes grew, the existing architecture couldn’t scale effectively, and the system lacked sufficient fault tolerance, monitoring, and alerting, affecting uptime and reliability. Additionally, there was a need for automation, compliance enhancements, and data governance to streamline operations and secure data, all while aiming to unlock new business use cases like predictive maintenance, performance optimization, and AI-driven insights.
Inefficient Real-Time Processing
Scalability Bottlenecks
Poor System Reliability
Solution
DataPebbles implemented a highly scalable, event-driven architecture with real-time processing and automation.
Technology Overhaul
• Optimized Data Handling with CrateDB for time-series analytics.
• Edge & Cloud Processing to balance workloads and reduce latency.
• Event-Driven Architecture using Kafka for real-time streaming.
Performance & Resilience Enhancements
• Optimized Data Flow with serialization, compression, and Kafka tuning.
• High Availability & Disaster Recovery with redundancy and automated backups.
• Advanced Monitoring & Security using Prometheus, Grafana, ELK, and Coreflux.
• Automated DevOps with CI/CD pipelines and infrastructure-as-code.
New Business Capabilities
• Predictive Maintenance leveraging AI to reduce downtime.
• Performance Optimization for improved energy efficiency.
• BI & Advanced Analytics for real-time decision-making and forecasting.
Business Impact
The business benefits from quicker decision-making, seamless scalability, and enhanced operational efficiency, driving growth and innovation. These improvements also ensure stronger system reliability and security.