Enhanced CSV Ledger Transformation and Reconciliation

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Enhanced CSV Ledger Transformation and Reconciliation

  • Day: 2025-11-10
  • Time: 16:40 to 17:10
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: In Progress
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: CSV, Data Transformation, Python, Reconciliation, SQL, Pandas

Description

Session Goal:

The session aimed to transform a wide CSV ledger into a normalized transactions table and develop a comprehensive data management strategy for CSV files, focusing on maintaining data integrity and enhancing reporting capabilities.

Key Activities:

  1. CSV Ledger Transformation: Developed a schema design and transformation rules to convert a wide CSV ledger into a normalized transactions table using SQL and pandas, ensuring data integrity through validation checks.
  2. Data Management Plan: Outlined a low-friction data management strategy for CSV files, including five guardrails to enhance data integrity and a pandas script for generating monthly aggregates.
  3. Reconciliation Script: Created a Python script to process CSV files for monthly financial reporting and internal reconciliation, ensuring legacy data remains intact.
  4. Function Updates: Updated the greedy_pair_match function to include ‘lax’ and ‘tight’ modes with adjustable tolerances, and improved the normalize_df function for better timezone handling.

Achievements:

  • Successfully designed a transformation process for CSV ledgers that ensures data integrity.
  • Implemented a low-friction data management plan with minimal changes to existing CSV structures.
  • Developed a robust reconciliation script for monthly financial reporting.
  • Enhanced existing functions to improve data processing and error handling.

Pending Tasks:

  • Further testing and validation of the new transformation and reconciliation processes.
  • Implementation of the updated functions in the production environment.

Evidence

  • source_file=2025-11-10.sessions.jsonl, line_number=1, event_count=0, session_id=73c6fe64d29699c5c984f97137ea3f811de6505d4c12c66a83c27f9c315d95a0
  • event_ids: []

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