Data transformation refers to the process of converting data from one format or structure into another. This is often necessary to prepare data for analysis, integration into databases, or to improve its usability and accessibility. Here are some common types of data transformations:
Filtering: Selecting a subset of data based on specified criteria. For example, filtering customer records to include only those from a specific region.
Sorting: Arranging data in ascending or descending order based on one or more attributes. For instance, sorting a list of products by price.
Aggregation: Combining multiple data points into a single value. Common examples include summing sales figures across different regions or calculating the average temperature over a period.
Normalization: Rescaling data to fit within a specific range, often between 0 and 1, to eliminate the impact of differing scales on analysis. This is useful in machine learning algorithms.
Denormalization: Adding redundant data or grouping data to optimize specific query performance, which is the opposite of normalization.
Pivoting: Rotating data from a state of rows to columns or vice versa. For example, converting a dataset with columns for each month's sales into a set of rows, each representing a month and listing sales figures across different products.
Merging: Combining data from two or more sources into a single dataset based on common attributes. This could involve merging customer information with purchase history.
Splitting: Dividing a single dataset into multiple parts based on certain criteria. For example, splitting a large log file into smaller files based on date ranges.
Encoding: Converting categorical data into a numerical format that can be used in statistical analysis or machine learning models. Techniques include one-hot encoding and label encoding.
Joins: Combining data from two tables based on a related column between them. Types of joins include inner join, left join, right join, and full outer join.
In the context of cloud computing, services like Tencent Cloud offer robust data processing and transformation capabilities through their Big Data & AI platforms, enabling users to efficiently manage and transform large datasets.