Principle:PrefectHQ Prefect CSV Data Loading
| Metadata | |
|---|---|
| Sources | pandas to_csv |
| Domains | ETL, Data_Engineering |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
A data persistence pattern that writes transformed tabular data to CSV files as the final loading step of an ETL pipeline.
Description
CSV Data Loading is the "Load" phase of ETL pipelines. It takes a transformed DataFrame and persists it to disk as a CSV file. This simple but effective pattern provides: human-readable output, compatibility with virtually all BI and analytics tools, and a checkpoint for data pipeline results. When wrapped as a Prefect task, the load step gains observability and can log a preview of the saved data.
Usage
Use this pattern as the final step of an ETL pipeline when the destination is a file-based format for downstream analysis, BI tool ingestion, or data archival.
Theoretical Basis
The Load phase completes the ETL cycle by persisting transformed data to a durable store. CSV is chosen for:
- Universal compatibility -- supported by virtually all data tools
- Human readability -- can be inspected with any text editor
- Simplicity -- no special libraries or drivers required
Pattern:
serialize(dataframe, format=CSV, path=output_file)
log_preview(dataframe)