📅 2025-09-24 — Session: Developed YAML-based data profiling and validation
🕒 21:35–22:25
🏷️ Labels: YAML, CSV, Data Profiling, Data Validation, Python
📂 Project: Dev
⭐ Priority: MEDIUM
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.