How Oowlish helped ExtractAlpha scale their data engineering pipelines and AI model infrastructure to process billions of financial data points daily.
ExtractAlpha provides alternative data and predictive signals to quantitative hedge funds and asset managers. They needed to scale their data processing infrastructure to handle exponentially growing datasets while maintaining the precision required for financial modeling.
ExtractAlpha's data pipelines were hitting capacity limits. Processing times for daily model updates were exceeding market-open deadlines, the infrastructure was costly and brittle, and adding new data sources required weeks of engineering effort.
Oowlish re-architected ExtractAlpha's data platform with modular pipelines on Apache Spark, implemented automated ML model lifecycle management, and migrated to a cost-optimized cloud infrastructure using spot instances and intelligent scheduling.
Modular Spark pipelines that process 10B+ data points daily with auto-scaling.
Automated model retraining, validation, and deployment with drift detection alerts.
Spot instance orchestration and intelligent scheduling reduced cloud spend by 40%.
ExtractAlpha now processes data 5x faster, adds new data sources in days instead of weeks, and reduced their infrastructure costs by 40% — all while maintaining the precision their hedge fund clients demand.
Pipeline processing speed increased 5x, completing well before market open.
Cloud infrastructure costs reduced through spot instances and smart scheduling.
New data source onboarding reduced from 4 weeks to 2 days.
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