Implemented Email Data Ingestion and MVP Architecture

📅 2025-10-01 — Session: Implemented Email Data Ingestion and MVP Architecture

🕒 15:20–16:30
🏷️ Labels: Data Ingestion, Postgresql, Mvp Architecture, Email Processing, Data Integration
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal: The session aimed to update the email data ingestion pipeline, optimize data extraction, and design a unified relationship graph MVP architecture for integrating messaging systems.

Key Activities:

• Successfully ingested approximately 88,000 rows into the emails2 database, ensuring idempotency and capturing bad rows for further analysis.
• Developed methods for querying PostgreSQL tables using pandas, enhancing data querying capabilities.
• Implemented an optimized method for extracting recent email records from PostgreSQL, focusing on performance improvements using Python.
• Provided a robust script for extracting email details with pandas, addressing common pitfalls such as missing columns.
• Outlined methods for downloading Google Takeout files to an external drive, troubleshooting common issues, and recovering lost Gmail exports.
• Designed the architecture for a Unified Relationship Graph MVP, integrating WhatsApp, Instagram, and Email, with a focus on data correctness and modularity.
• Detailed data contracts and adapter mappings for messaging sources, ensuring data normalization and integrity.

Achievements:

• Completed the email data ingestion pipeline with enhanced performance and reliability.
• Established a comprehensive architecture for a messaging system MVP, setting the foundation for future expansions.
• Improved data extraction and querying processes, leading to more efficient data handling.

Pending Tasks:

• Further testing and validation of the data ingestion strategy for messaging adapters.
• Implementation of data contracts and adapter mappings for full integration across platforms.