Principle:Mbzuai oryx Awesome LLM Post training Data Export Pipeline
| Knowledge Sources | |
|---|---|
| Domains | Data_Collection, Data_Export |
| Last Updated | 2026-02-08 07:30 GMT |
Overview
A multi-format data export strategy that converts collected structured data into both human-readable JSON and tabular Excel formats.
Description
The Data Export Pipeline principle addresses the need to produce collection outputs in multiple formats suited to different downstream consumers. Raw JSON preserves the full hierarchical structure (including nested references and citations), while a flattened Excel spreadsheet enables quick browsing, filtering, and sharing with collaborators who may not work with JSON directly.
The key challenge is structure flattening: academic paper data often has nested fields (lists of authors, nested reference trees) that must be intelligently normalized into flat table columns for spreadsheet export.
Usage
Use this principle at the final stage of any data collection pipeline when:
- Collected data has a hierarchical or nested structure
- Multiple output formats are needed (archival JSON + tabular spreadsheet)
- Downstream consumers include both programmatic users (JSON) and manual reviewers (Excel)
Theoretical Basis
The export pipeline follows a two-stage pattern:
Pseudo-code Logic:
# Abstract multi-format export (NOT real implementation)
# Stage 1: Preserve full structure
save_json(data, "output.json")
# Stage 2: Flatten and export to tabular format
flat_data = normalize_nested_structures(data)
save_spreadsheet(flat_data, "output.xlsx")
The json_normalize operation converts nested dictionaries into flat rows by creating dotted column names for nested fields (e.g., tldr.text becomes a column header).