pythondata-engineeringfacebookimessagepipelinepersonal
November 2025 Built

Friend Relationship System v2.0

Multi-platform data engineering pipeline that ingests exported data from 6 communication platforms, deduplicates across sources, scores relationship strength via interaction frequency and recency, and generates a tiered priority dashboard. 4,000+ contacts processed.

Built with

Python
Python

The Problem

I had 170+ meaningful relationships scattered across 8 platforms with zero unified view. Who was I actually talking to? Who had I drifted from? Who needed a call?

The Solution

A Python orchestration pipeline that ingests raw exports from every major platform, deduplicates contacts across sources, scores relationship strength, and outputs a structured tiered database.

Data Sources (v2.0)

SourceRecords
Facebook Messenger1,763 conversations
iMessage2,474 contacts
Instagram DMs95 conversations
GmailThreading analysis
Google CalendarEvent co-attendance
LinkedInConnection graph

Output

  • 170+ friends discovered across all sources
  • Three tiers: Tier 1 Active (48), Tier 2 Maintain (62), Tier 3 Loose Ties (60)
  • Priority dashboard — sortable CSV with last contact, message volume, platform mix
  • Auto-tiering — scoring model weights recency, frequency, and bidirectionality

What I Found

Running this on myself revealed patterns I couldn’t see manually:

  • Top 5 relationships = 36% of all message volume
  • 4 close contacts went completely silent mid-2025 simultaneously (“late summer dropout”)
  • My communication style splits cleanly into text-primary and call-primary clusters
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