📅 2025-06-28 — Session: Comprehensive ETL Pipeline and Automation Execution
🕒 08:30–09:55
🏷️ Labels: ETL, Python, Automation, Data Processing, CSV
📂 Project: Dev
⭐ Priority: MEDIUM
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.