Developed YAML-based data profiling and validation

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Developed YAML-based data profiling and validation

  • Day: 2025-09-24
  • Time: 21:35 to 22:25
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: YAML, CSV, Data Profiling, Data Validation, Python

Description

Session Goal

The session aimed to enhance data processing workflows by integrating YAML-based configurations for data profiling and validation, focusing on CSV files.

Key Activities

  • Structured the Censo to EPH Adapter: Organized components between shared aligners and ML repositories, encoding mappings in YAML.
  • Bash Scripts for CSV Header Extraction: Developed scripts to extract headers from CSV files, handling delimiters and formatting.
  • Overview of CPV2010 Data Model: Reviewed the CPV2010 data model, documenting it in a YAML schema with relational database insights.
  • Profiling CSV Data Types: Implemented methods for CSV data profiling using Python, CLI, and Rust tools for schema inference.
  • Optimizing Data Storage and Validation: Applied best practices for data type management and validation in YAML for pandas DataFrames.
  • Systematic Data Type Profiling: Used Dask to determine optimal data types and enforce global policies for data consistency.
  • CSV Profiling with Dask: Created a notebook cell for profiling all CSV files, generating JSON suggestions for data types.
  • YAML Column Type Mappings: Provided YAML configurations for data types based on profiling results, integrating loaders into Python code.
  • Consolidate CSV Labels into YAML: Consolidated CSV files into a YAML format, ensuring robust handling of headers and encodings.
  • Data Structure Review: Conducted a review of data structures, recommending improvements for consistency and safety.

Achievements

  • Successfully developed and integrated YAML-based data profiling and validation workflows.
  • Enhanced data processing efficiency and accuracy through structured data management practices.

Pending Tasks

  • Further integration of YAML loaders into existing Python workflows.
  • Continuous refinement of data validation checks and coding practices.

Evidence

  • source_file=2025-09-24.sessions.jsonl, line_number=6, event_count=0, session_id=eca83b7a04117912a0831524281dbca4164aae04a2e8fae677498961f05f759c
  • event_ids: []

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