Sports Odds Data Pipeline
I built an end-to-end, serverless AWS pipeline that ingests sportsbook odds, normalizes and validates records, stores both raw + curated datasets, and serves low-latency API responses for a live UI. The site auto-deploys via GitHub Actions to S3/CloudFront, and the data API is backed by API Gateway + Lambda + DynamoDB.
What this demonstrates
- Event-driven ingestion + serverless ETL
- Data modeling for query-efficient reads
- CI/CD automation (infra + app)
- Production-style concerns: CORS, caching, least privilege, and reliability
Tech AWS (S3, CloudFront, API Gateway, Lambda, DynamoDB, EventBridge, Glue), Python, Terraform, GitHub Actions
Outcomes Reliable ingestion → normalized data → low-latency API → live UI table
Live CI/CD enabled via GitHub Actions → S3 sync → CloudFront cache invalidation (automatic deploy on push)
Architecture Diagram: High-Level
Live Demo: Today's Games + Custom Bet
This table is generated dynamically from my API (API Gateway + Lambda) querying DynamoDB for today’s slate.
All times are in your local timezone.
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