Streamlined ETL and DHS Data Handling

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Streamlined ETL and DHS Data Handling

  • Day: 2024-11-06
  • Time: 15:00 to 16:15
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: In Progress
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: ETL, DHS, Data Analysis, Python, SQL

Description

Session Goal:

The session aimed to streamline ETL processes using SQL and enhance DHS data handling techniques for socio-economic analysis.

Key Activities:

  • Reviewed methods to capture clipboard history across different operating systems to improve productivity.
  • Explored a typical ETL workflow using SQL, including data extraction, cleaning, and initial transformation steps.
  • Discussed tips for managing LinkedIn posts and integrating them into ETL processes for data engineering.
  • Assisted in DHS data analysis by aligning dataset variables and verifying file paths for CSV files.
  • Converted .DO and .DCT files to .CSV using Python and Stata, ensuring variable presence in the resulting files.
  • Handled .dta files using pandas and explored methods for loading .DAT files using .DCT specifications.
  • Addressed missing data columns for socio-economic analysis and outlined steps for data retrieval and merging.

Achievements:

  • Finalized a list of variable names for DHS data analysis, identifying key socio-economic and health indicators.
  • Developed a modular approach to load data from .DCT and .DAT files, facilitating data verification.
  • Provided guidance on handling DHS weights in data analysis to ensure accurate population representation.

Pending Tasks:

  • Further exploration of clipboard management tools to enhance cross-platform productivity.
  • Complete the integration of LinkedIn data into ETL workflows for comprehensive data analysis.

Evidence

  • source_file=2024-11-06.sessions.jsonl, line_number=2, event_count=0, session_id=d3841381d841c3a2356956e814bc886acb6a62e6048896017d942e3520ea67b3
  • event_ids: []

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