SpringCo
Developing Scalable Data Platform For Spring Co
SpringCo, an emerging leader in urban analytics, envisioned creating a scalable data platform to empower city planners and analysts with real-time insights. The goal was to harness vast urban data streams from IoT devices, city sensors, and APIs to improve urban planning and infrastructure management. However, SpringCo lacked the foundational infrastructure to bring this data-driven vision to life. SymuFolk partnered with them to build a scalable, automated, and efficient data platform that supports real-time analytics.
Total Hours
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Resultados
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Reduced Operation Cost
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Taxa de satisfação
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Desafios
SpringCo faced several key challenges in developing its urban analytics platform:
- No Existing Data Infrastructure: There was no foundational system in place to support their analytics vision.
- Complex Data Integration: Multiple data sources—IoT devices, sensors, and APIs—needed to be unified into a centralized platform.
- Scalability Issues: The platform had to support large data volumes while remaining flexible for future growth.
- Real-Time & Batch Processing: The system needed to manage both real-time and historical data ingestion efficiently.
- Data Accessibility: Urban planners require fast, seamless access to actionable insights with minimal latency.
Solução
SymuFolk developed an end-to-end data pipeline leveraging AWS services to automate data ingestion, transformation, and storage. AWS Batch was used for large-scale data processing, while AWS Lambda automated pipeline tasks, ensuring smooth execution.
The solution seamlessly integrated IoT data, sensor feeds, and APIs into a centralized repository. By implementing query-optimized storage, we enabled real-time and historical data access, providing city planners with instant insights for better decision-making.
nossa contribuição
Pitão
GráficoQL
Node.js
NestJS
PHP
Microsoft Net
RESULTADOS
SymuFolk’s scalable data platform transformed urban analytics for SpringCo, enhancing efficiency, scalability, and real-time decision-making while ensuring seamless data accessibility.
SymuFolk’s scalable data platform transformed urban analytics for SpringCo, enhancing efficiency, scalability, and real-time decision-making while ensuring seamless data accessibility.
90%
90%
Manual Intervention
Automation minimized the need for manual processes, improving efficiency and reducing errors.
50%
50%
Data Processing
The platform processed urban data twice as fast, enabling real-time decision-making.
70%
70%
Scalability Improvement
The platform seamlessly scaled to handle a growing volume of data sources, ensuring it could meet future demands.
como fizemos isso
Requirements Analysis
Conducted an in-depth review of SpringCo’s data sources, processing needs, and end-user requirements.
Platform Design
Developed a custom architecture to integrate diverse urban data sources into a scalable system.
Data Pipeline Development
Built automated data pipelines using AWS Batch and Lambda to manage real-time and batch processing.
Optimized storage & access
Designed query-optimized storage to ensure fast and seamless data retrieval.
Urban Planners Empowered
Provided instant insights for city planners and analysts.
Test & Optimization
Rigorously tested the platform to ensure performance, scalability, and efficiency under real-world conditions.
Tecnologias
NÓS USAMOS
NÓS USAMOS
Avaliação Inicial
Conducted an in-depth review of SpringCo’s data sources, processing needs, and end-user requirements for urban analytics.
Solution Design
Developed a custom, scalable architecture, integrating IoT devices, sensors, and APIs into a centralized data platform.
Implementation & Automation
Built automated data pipelines using AWS Batch and Lambda, enabling real-time and batch data processing with minimal manual intervention.
Optimization & Performance Tuning
Designed query-optimized storage for fast and seamless data retrieval, ensuring 99.9% uptime and 50% faster processing.
Ongoing Monitoring & Support
Implemented real-time monitoring, ensuring continuous system scalability, data accuracy, and long-term operational efficiency.
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