adattranszformációs

Adattranszformáció refers to the process of converting data from one format or structure to another. This is a common task in data processing and analysis, often performed to make data compatible with different systems, improve its quality, or prepare it for specific analytical techniques. The transformation can involve various operations such as changing data types, restructuring data tables, cleaning or imputing missing values, aggregating or disaggregating data, or encoding categorical variables.

The necessity for adattranszformáció arises in numerous scenarios. For instance, when integrating data from multiple sources, each might use a different schema or representation, requiring alignment. Similarly, before feeding data into machine learning models, it often needs to be transformed into a numerical format or scaled appropriately. Data warehousing and business intelligence also heavily rely on adattranszformáció to create consistent and usable datasets for reporting and analysis.

Common techniques within adattranszformáció include normalization and standardization for numerical data, one-hot encoding for categorical data, and pivoting or unpivoting for restructuring tables. Data cleaning, which involves handling errors, inconsistencies, and missing values, is often a prerequisite or an integral part of the transformation process. The goal is to ensure that the transformed data is accurate, consistent, and suitable for its intended purpose.