SpringCo
Developing Scalable Data Platform For Spring Co
SpringCo, an emerging leader in urban analytics, aimed to revolutionize decision-making for city planners and analysts by creating a scalable data platform. The company envisioned harnessing vast streams of urban data to deliver real-time, actionable insights. However, they faced significant challenges in creating an infrastructure that could handle this data-driven vision.
Challenges
- No Existing Data Infrastructure: SpringCo lacked the necessary infrastructure to support its vision of an advanced urban analytics platform.
- Data Integration: Multiple diverse data sources, including IoT devices, city sensors, and APIs, needed to be integrated into a single, unified system.
- Scalability Issues: The platform needed to be scalable to handle the growing volume of data from various urban sources.
- Real-time and Batch Data Processing: The solution needed to manage both real-time and batch processing for a wide array of data formats.
- Data Accessibility: Ensuring seamless access to data for urban planners, with minimal latency and maximum usability, was a priority.
Solution
We built an end-to-end data pipeline using AWS services to enable scalable, automated data ingestion, transformation, and storage. AWS Batch was utilized for large-scale data processing, while AWS Lambda automated pipeline tasks to ensure smooth execution. We seamlessly integrated diverse urban data sources, including IoT, sensors, and APIs, into a centralized repository.
The pipeline was designed to optimize data access by enabling real-time and historical data ingestion, supported by advanced orchestration and query-optimized storage. This setup allows urban planners and analysts to easily access and analyze data for better decision-making.
our input
Python
GraphQL
Node.js
NestJS
PHP
Microsoft Net
RESULTS
We’re proud of reaching new heights with our customers,
helping them achieve advanced levels of scalability and stability.
We’re proud of reaching new heights with our customers,
helping them achieve advanced levels of scalability and stability.
90%
90%
Manual Intervention
50%
50%
Data Processing
70%
70%
Scalability Improvement
how we did it
Requirements Analysis
Platform Design
Data Pipeline Development
Optimized storage & access
month to launch
Test & Optimization
Technologies
WE USED
