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Best practices for loading files into a chain — single file, folder globs, type detection, and union of many files. Use when the pipeline starts from CSV/Parquet/JSON/Excel files or a folder of files.
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基于 SOC 职业分类
| name | Patrones de Ingesta |
| description | Best practices for loading files into a chain — single file, folder globs, type detection, and union of many files. Use when the pipeline starts from CSV/Parquet/JSON/Excel files or a folder of files. |
| keywords | ingesta, cargar, importar, import, csv, parquet, json, excel, carpeta, folder, archivos, union, glob, fuente, source, leer |
| next | data-cleaning-pipeline |
| scope | engineering |
Activa al inicio de un flujo cuando la fuente son archivos. Elige el patrón por la forma de los datos en disco.
auto_detect para CSV; especifica el tipo si la extensión miente.*.csv) — usa union_by_name=true por debajo, así columnas en distinto orden no rompen la unión.raw_ventas), no el default. Las fuentes son datos del usuario y viven en main.delimiter y skip rows en la pestaña Advanced antes de pelear con la limpieza.${input_path}.Tras ingestar, encadena [[data-cleaning-pipeline]] y, si hay varias fuentes, [[multi-source-merge]].
Sequence cleaning steps in a chain — trim/case/replace, null handling, deduplication, and type casting — in the right order. Use when raw ingested data needs cleaning before joins or aggregation.
Place Assert and Schema Validation nodes as gates that halt a chain when data is wrong, so bad data never reaches the output. Use when the pipeline must guarantee correctness before exporting or loading downstream.
Choose the right sink format, compression, and destination for a chain's output, and decide between exporting a file vs creating a table. Use at the end of a pipeline when deciding how to persist results.
Decide between Merge (stack rows / UNION) and Join (match on a key) when a chain has multiple inputs, and set keys and join type correctly. Use when a pipeline combines two or more upstream tables.
Descompone un objetivo de procesamiento en un flujo de nodos Chains (source → transform → sink). Use when the user wants to build a data pipeline / chain, or describes an end-to-end "load X, clean it, summarize, export" goal.
Framework de razonamiento para convertir resultados de análisis en una narrativa visual clara y convincente