Add support for Extraction Task
- Lenguaje dominante
- Python
- Estrellas
- 26
- Forks
- 3
- Métricas de merge de PR
- Sin PR fusionados en 30 d
Descripción
## Summary
> Add support for parsing various columnar data types and extend functionality with enrichment, sampling, and mapping parameters.
## Pain Points
> What challenges are users encountering without this feature?
1. Current systems may not support parsing a range of columnar data types like JSON, Parquet, and CSV, limiting data integration and usability.
2. Lack of enrichment, sampling, and mapping parameters complicates data preparation and manipulation, potentially leading to inefficient workflows.
## Current Workarounds
> Have you come across any alternatives or tried any workarounds?
1. Users might manually preprocess or convert data to compatible formats before ingestion.
2. Custom scripts or external tools might be used to handle data enrichment, sampling, and mapping, but these can be complex and inconsistent.
## Solution
> What solution would you like to see?
1. Add support for parsing columnar data types including JSON, Parquet, and CSV.
2. Implement parameters for:
- Enrichment to enhance data with additional information.
- Sampling to manage and analyze subsets of data.
- Mapping, including many-to-one mapping parameters, to handle complex data transformations and relationships.
## References
> Have you seen similar solutions or examples elsewhere?
1. Review data processing libraries or frameworks that support parsing columnar data types and offer parameterized data manipulation.
2. Refer to documentation on enrichment, sampling, and mapping techniques for best practices and implementation guidance.
## Additional Notes
> Is there anything else you’d like us to know?
1. Ensuring robust support for these features will significantly enhance the flexibility and capability of data handling processes.
## Contribution
- [ ] I’d love help with this if needed!
Guía de contribución
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Línea de trabajo
The issue describes adding parsing for JSON, Parquet, and CSV, plus enrichment, sampling, and mapping parameters. Look at the existing data ingestion and transformation modules in the codebase to understand the current architecture. Identify where columnar data parsing is handled and where new parameters could be integrated. Review similar data processing libraries for implementation patterns.
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Evaluación
- Stack tecnológico
- python
- Área
- data-engineering
- Tipo de issue
- Nueva funcionalidad
- Dificultad
- 4/5
- Tiempo estimado
- 3-5 días
- Estado de actividad
- Estancado
- Claridad
- Necesita aclaración
- Aptitud para principiantes
- 25/100