Comprehensive ETL Pipeline and Automation Execution

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Comprehensive ETL Pipeline and Automation Execution

  • Day: 2025-06-28
  • Time: 08:30 to 09:55
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: ETL, Python, Automation, Data Processing, CSV

Description

Session Goal

The primary objective of this session was to execute and refine a comprehensive ETL (Extract, Transform, Load) pipeline using Python, with a focus on automation and data processing for financial reporting.

Key Activities

  • Developed and executed Python scripts for ETL processes, handling data from Google Sheets and generating CSV reports.
  • Implemented a strategy for regenerating plots programmatically from CSV files using modular functions in Python.
  • Adjusted PeriodIndex to a datetime-based index in the ETL script to align output with transaction dates.
  • Fixed issues related to column selection and ValueError in CSV export, ensuring correct data processing and export.
  • Provided solutions for improving timestamp indexing in financial pivot generation.

Achievements

  • Successfully created and executed a full ETL pipeline script, generating various reports and time series outputs.
  • Enhanced data processing accuracy by addressing indexing and column selection issues.
  • Established a reproducible system for ETL and analysis regeneration, incorporating automation strategies.

Pending Tasks

  • Further enhancements to the ETL pipeline, such as integrating a Makefile, scheduler, or Jupyter Notebook version for more robust automation.
  • Continued refinement of [[data visualization]] strategies and plotting scripts for improved insights.

Evidence

  • source_file=2025-06-28.sessions.jsonl, line_number=0, event_count=0, session_id=8739908d84cfe94102c68ec37db765927725c71f832edba5e35dbb6c4bbe50aa
  • event_ids: []

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