Data Scraping Encyclopedia>Data Scraping Noun> Data Transformation

Data Transformation | Web Scraping Tool | ScrapeStorm

2025-07-28 14:16:47

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Abstract：Data Transformation is the process of converting data of one format, structure, and content into a different format or structure. ScrapeStormFree Download

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Introduction

Data Transformation is the process of converting data of one format, structure, and content into a different format or structure. It is essential for smooth data integration, migration, analysis, visualization, and inter-system integration. Transformations range from simple format conversions (e.g., CSV to JSON) to complex semantic conversions (e.g., unit conversions and code system unification), and even geographic coordinate and spatial schema conversions. Data transformation is a central element of the ETL (extract, transform, load) process and is crucial for the integration of heterogeneous data held by companies and public organizations.

Applicable Scene

Data Transformation is widely used to link data between different systems, migrate from old systems to new systems, or as pre-processing for data analysis.

Pros: First, data transformation ensures compatibility between disparate systems, making it easier to integrate and reuse information. Second, the standardization of data structures dramatically improves the efficiency of analysis, visualization, and report creation. Furthermore, transformation processes such as removing noise and redundant information and normalizing data improve data quality, contributing to improved accuracy of AI and machine learning models. In addition, transformation that absorbs differences in specifications by language and region makes international expansion and multi-platform support smoother.

Cons: Data transformation often requires high design and implementation costs, and it takes time and resources to design conversion logic, especially when dealing with large-scale systems and diverse formats. There is also a risk that some data may be lost or distorted during the transformation process, especially if the compatibility between formats is not complete. Furthermore, if the transformation process becomes a black box, it may be difficult to trace the origin and meaning of the data, and there may be issues with audits and traceability. In some cases, transformation errors may affect the entire business process, so careful management is required.