01 / 05 Case study

Scraper

Extraction, normalization, persistence, and data workflows.

02 / 05 What it is

Historical research and development into data extraction workflows

Extraction, normalization, and persistence work as one pipeline that turns inconsistent sources into stable structures for integrations, analysis, and automation.

Core stack

  • Extraction
  • Normalization
  • Persistence
  • Data Workflows

03 / 05 What it proves

Useful data begins after extraction, not at extraction

  • Collection, normalization, and persistence are treated as one data workflow.
  • Messy source formats are converted into stable structures that downstream systems can use.
  • The work established reusable patterns for later integration and automation systems.

04 / 05 Related paths

05 / 05 The next move

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