GeoAnalytics
Industry
Insurance
Headquarters
NYC, United States
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Background
GeoAnalytics is a pioneering provider of geospatial data analytics solutions, dedicated to delivering advanced tools for visualizing, analyzing and managing risk and location-based data. Serving industries such as insurance, finance and real estate. GeoAnalytics empowers organizations to make data-driven decisions using high-resolution geospatial data, optimize asset management and mitigate risk. Prior to partnering with Benchmark IT Solutions, GeoAnalytics initially relied on freelancers but faced challenges in achieving consistent technical performance and scalability. Frequent system breakdowns, unstable releases and slow support impacted their ability to handle large datasets effectively for a growing customer base. Seeking a solution to improve application stability, enhance scalability and provide timely support, GeoAnalytics partnered with Benchmark IT Solutions to transform their platform into a reliable, high-performance solution that could seamlessly meet customer demands.
Challenges faced
Scalability and Performance Limitations
The current architecture supported up to 200,000 data points but needed to scale to handle 2.5 million data points with low latency. High processing times and data import delays also limited user interactions and timely data availability.
Inefficient Data Import and Batch Processing
Import processes required significant optimization, with current processing times extending to several hours for large datasets. The Java Stream API used for batch processing limited efficiency, necessitating a more parallelized approach.
System Bottlenecks and Single Points of Failure
Core components, including Geoserver, RabbitMQ, and the Tomcat application server, were single points of failure, resulting in response delays and system bottlenecks during peak data demands, limiting uptime and user access during critical times.
Caching and Data Loss on Restarts
The in-memory caching approach lacked persistence, leading to data loss during restarts and limiting scalability. A scalable, distributed caching solution was essential for reliable data storage and retrieval.
Defined Solution
Scalable and Optimized Data Handling
GeoAnalytics adopted a distributed architecture to improve scalability and performance by replacing the Java Stream API with optimized batch processing and enhancing Geoserver with load balancing and middleware for efficient batch API handling. These upgrades reduced response times and boosted data processing efficiency.
Persistent and Distributed Caching
Redis was integrated as a distributed caching solution to ensure data persistence, prevent data loss during restarts and reduce latency. This scalable solution supports reliable data storage, improving both performance and scalability.
Data Import Optimization with Real-Time Processing
GeoAnalytics accelerated data imports with a parallel processing framework using Java’s Fork/Join and integrated Kafka for real-time data ingestion, supporting immediate data availability and faster processing times.
Enhanced Tile Rendering and Client-Side Optimization
The platform was optimized for improved map interactions by converting raster tiles to vector tiles using Mapbox Studio, reducing data size and improving render speeds. Client-side rendering with Mapbox GL JS enabled fast, smooth interactions, greatly enhancing the user experience.
Load Balancing and Microservices for Improved Resilience
Transitioning to a microservices-based architecture, GeoAnalytics scaled RabbitMQ with additional consumers and implemented NGINX load balancing for efficient request distribution, supporting seamless scaling and resource management.
Advanced Monitoring and Maintenance
Real-time monitoring through Prometheus and Grafana provided insights into system health and performance. Automated alerting enabled proactive issue detection, ensuring consistent uptime and efficient troubleshooting.
The Outcome
The Impacts
Scalable Infrastructure
Optimized to handle up to 2.5 million data points with minimal latency.
Faster Data Availability
Automation reduced data import times by 75%, ensuring quicker access.
Reliable Performance
Distributed caching and load balancing improved system resilience.
Enhanced User Experience
Optimized rendering and interactive maps delivered seamless interactions.
