Building an Alternative Data Pipeline for Investment Research

Alternative data has become increasingly important for investment research, providing insights that traditional financial data cannot capture. In this post, I'll share my experience building alt-data pipelines for hardware and raw materials tracking.

The Challenge

Investment analysts often rely on scattered web sources, vendor feeds, and industry reports to track market trends. The goal was to build a system where "what analysts see is what AI sees" - ingesting these diverse sources into unified, trackable datasets.

Data Sources

We focused on several key categories:

• Memory prices: DRAM/NAND contract prices, DDR4/DDR5 spot prices
• Semiconductor materials: Chip/wafer prices, silicon pricing
• Rare metals: Ga/In/Ge (Gallium, Indium, Germanium) for semiconductor applications
• Base metals: LME and SMM non-ferrous metal time series

Architecture Design

The pipeline uses MongoDB + object storage as a lightweight data lake. This approach offers flexibility for semi-structured data while maintaining query performance for time series analysis.

ETL Pipeline Components

• Web crawlers with rate limiting and retry logic
• Schema standardization layer
• Update frequency management (daily/weekly)
• Data quality validation

Derived Indicators

Beyond raw price collection, we designed indicators to monitor:

• Level: Current price positioning relative to historical ranges
• Slope: Rate of change for trend identification
• Cross-sectional spreads: Price differentials across products (e.g., DDR4 vs DDR5)

Key Learnings

Building alt-data pipelines taught me that data engineering for finance is as much about domain understanding as technical implementation. Knowing which price movements matter and how analysts use the data shapes every design decision.

The best data infrastructure is invisible to analysts - they just see clean, timely data ready for analysis.