adatátalakítások

Adatátalakítás, often translated as data transformation, refers to the process of converting data from one format or structure to another. This is a fundamental step in many data-related workflows, including data warehousing, data mining, and data integration. The primary goal of data transformation is to make data more suitable for a specific purpose, such as analysis, reporting, or machine learning model training.

Common types of data transformations include cleaning, where inconsistencies, errors, and missing values are addressed; normalization and standardization, which adjust the scale of numerical data to a common range; aggregation, where data is summarized at a higher level; and enrichment, where new information is added to existing data. For example, a date might be transformed from a string format like "2023-10-27" into a datetime object for easier manipulation. Similarly, text data might be converted into numerical representations using techniques like one-hot encoding or embedding.

The choice of transformation techniques depends heavily on the source data characteristics and the intended use of the transformed data. Proper data transformation is crucial for ensuring data quality, improving the efficiency of data processing, and enabling accurate insights from data analysis. Without effective transformation, data can be difficult to work with, leading to inaccurate conclusions and flawed decision-making.