Adattranszformációk

Adattranszformációk refer to the process of converting data from one format or structure to another. This is a fundamental step in data processing, analysis, and integration. The primary goal of data transformation is to make data more suitable for its intended use, whether that involves improving its quality, enabling its use in a different system, or preparing it for a specific analytical model.

Common types of data transformations include cleaning, normalization, aggregation, and enrichment. Data cleaning involves correcting errors, handling missing values, and removing duplicates. Normalization rescales data to a standard range, which is often crucial for machine learning algorithms. Aggregation summarizes data by grouping it and performing calculations like sums or averages. Data enrichment involves adding new information to existing data, for example, by combining it with external datasets.

The need for data transformations arises in various scenarios. When integrating data from multiple sources, transformations are necessary to ensure consistency in formats and structures. In data warehousing, data is transformed before being loaded into the repository to meet analytical requirements. For data mining and machine learning, raw data often needs to be transformed into a suitable format for model training. The specific transformations applied depend heavily on the nature of the data and the objectives of the process. Effective data transformation is key to unlocking the value of data.