BYOD in the real world (adapters, exports, privacy)

Why this exists: Students need a ‘real export’ experience. This chapter frames BYOD as a repeatable, safe workflow.

Learning objectives

  • Explain what adapters do and why we prefer them to manual spreadsheet cleaning.

  • Know the BYOD commands: pystatsv1 trackd byod init, pystatsv1 trackd byod normalize, pystatsv1 trackd validate, pystatsv1 trackd byod daily-totals.

  • Handle privacy safely (what to redact and how to share).

Outline

The BYOD workflow

  • Initialize a project folder with templates.

  • Drop in an export under tables/ (source-specific).

  • Normalize to canonical outputs under normalized/.

  • Run analysis helpers and Track D scripts on normalized outputs.

  • Adapters keep the cleanup step repeatable and testable — no one-off spreadsheet edits.

Adapters and tradeoffs

  • passthrough: already canonical data.

  • core_gl: generic GL export cleaning.

  • gnucash_gl: specific to GnuCash multi-line export.

Privacy + classroom sharing

  • Never publish raw exports that include names, addresses, invoice details.

  • Prefer aggregated outputs (daily totals) for sharing examples.

  • If you must share, redact names, shorten the date range, and consider rounding amounts.

Where this connects in the workbook

Note

This page is intentionally an outline right now. Expand it incrementally as we refine Track D narrative.