How isezdasgith/overview-of-hyperscaler-capital-expenditure-and-memory-demand works
This repository is an exploratory notebook analysis of whether hyperscaler capital spending is reflected in Nvidia data-center revenue and, with a lag, Micron memory revenue and margins. The documented workflow compares those datasets and uses DRAM market share as supporting context. The notebook’s implementation was not excerpted, so relationships below rely on README descriptions rather than inspected code.
Main entry points: hyperscaler_capex_memory_analysis.ipynb Analysis Notebook · Hyperscaler Financials Data - Cleaned Data.csv Hyperscaler Financials · nvidia_quarterly_revenue_segment_cleaned.csv Nvidia Segment Revenue · micron_financials_cleaned.csv Micron Financials
Components 7
Research Data
- Hyperscaler Financials
data/Hyperscaler Financials Data - Cleaned Data.csv - Nvidia Segment Revenue
data/nvidia_quarterly_revenue_segment_cleaned.csv - Micron Financials
data/micron_financials_cleaned.csv - DRAM Market Share
data/dram_market_share_counterpoint.csv
Analysis
- Analysis Notebook
hyperscaler_capex_memory_analysis.ipynb
Reported Results
- Exploratory Findings
readme.md
Other components
- Researcher
Connections 6
- Researcher → Analysis Notebook: runs analysis
readme.md - Hyperscaler Financials → Analysis Notebook: provides capex
readme.md - Nvidia Segment Revenue → Analysis Notebook: provides revenue
readme.md - Micron Financials → Analysis Notebook: provides financials
readme.md - DRAM Market Share → Analysis Notebook: adds context
readme.md - Analysis Notebook → Exploratory Findings: reports results
readme.md