Resolved ABI Mismatch and Optimized Dask Operations

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📅 2025-09-24 — Session: Resolved ABI Mismatch and Optimized Dask Operations

🕒 19:50–21:00
🏷️ Labels: Python, Dask, GCP, Data Publishing, Git
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal: The session aimed to resolve ABI mismatches in Python environments and optimize Dask DataFrame operations for improved performance.

Key Activities:

  • Resolved ABI mismatches by aligning version matrices for NumPy, pandas, and SciPy, using both Conda and venv for environment management.
  • Implemented a run banner in CLI scripts to enhance log readability and traceability.
  • Optimized Dask DataFrame operations by replacing slow isin calls with merge-based semi-joins and introduced progress bars for long-running tasks.
  • Explored data publishing models for the Argentina Census 2010, recommending a hybrid strategy for effective data dissemination.
  • Developed a minimum viable publishing plan for GCP, including dataset preparation, uploading, and serving via a custom domain.
  • Managed Git repository with improved .gitignore, staged commits, and documentation enhancements.

Achievements:

  • Successfully resolved ABI mismatches and optimized Dask operations, leading to improved performance and traceability in Python scripts.

Pending Tasks:

  • Further refinement of the GCP publishing workflow for the Argentina Census 2010 data.
  • Complete the design and implementation of a harmonization layer for survey data using a Canonical Data Model.
  • Finalize README structure for the Census Sampler repository.

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