Scalable Data Aggregation & Normalization Engine
Project Overview
Architected a high-throughput data ingestion system aggregating structured records from multiple dynamic platforms. The system ensures consistent normalization, incremental updates, and reliable downstream delivery.
Business Context
Data was distributed across heterogeneous platforms with no unified schema. Frequent updates caused duplication and stale records, making naive scraping approaches unreliable.
Solution
Designed an asynchronous ingestion pipeline with validation, normalization, and incremental synchronization logic to maintain data integrity and operational stability.
Architecture Highlights
- Async ingestion workers (asyncio + multiprocessing)
- Source-specific adapters for heterogeneous platforms
- Central normalization layer ensuring schema consistency
- Incremental upsert strategy preventing duplicate records
- Fault-tolerant retry and backoff mechanisms
Tech Stack
Results
- 12,000+ structured records aggregated
- 5–8× performance improvement vs synchronous baseline
- <1% failure rate under load
- Reduced redundant processing via incremental logic