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Local data pipeline

Scout

Find the listings that actually changed.

PythonhttpxSQLiteCSV
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Scout system architectureSYSTEM ARCHITECTURE

THE PROJECT

Job collection, filtering, deduplication and change reporting through a local pipeline.

Source connectors collect listings into a common format. Filters narrow the results, persistent identifiers reduce duplicates and stored snapshots distinguish new opportunities from meaningful changes. CSV, digest and configured spreadsheet outputs turn the collected data into a usable workflow.

  • Pluggable job-source connectors
  • Normalization, filters and change detection
  • Local browser interface, CSV and optional Sheets

Independent project built as Job Board Scraper. Spreadsheet output requires its own configuration.

IN MOTION

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13 s
Find the listings that actually changed.Scout: a local pipeline for collecting and comparing listings.Text animation for the Scout project: its tagline, then a numbered list of the six parts of the system, from source connectors to export & digest, then the FutureInvoked end card with a one-line summary.

UNDER THE SURFACE

How it fits together.

Source-specific collectors feed a common normalization pipeline. Persistent snapshots support deduplication and change-aware outputs.

Swipe or scroll the diagram to inspect it. The components are also listed below.

Scout simplified architecture: Source connectors; Normalize & filter; Deduplicate & compare; SQLite snapshots; Local interface; Export & digest
  1. 01
    Source connectors

    Job listing feeds

  2. 02
    Normalize & filter

    Shared record format

  3. 03
    Deduplicate & compare

    Identity and changed fields

  4. 04
    SQLite snapshots

    Persistent listing history

  5. 05
    Local interface

    Review collected matches

  6. 06
    Export & digest

    CSV or configured Sheets

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