Data transformation is a technique of conversion as well as mapping of data from one format to another. The tools and techniques used for data transformation depend on the format, complexity, structure and volume of the data.
It enables a developer to translate between XML, non-XML, and Java data formats, for rapid integration of heterogeneous applications regardless of the format used to represent data.
Here, we have listed the top eight data transformation methods in alphabetical order.
AIM Daily XO
Join our editors every weekday evening as they steer you through the most significant news of the day, introduce you to fresh perspectives, and provide unexpected moments of joy
Your newsletter subscriptions are subject to AIM Privacy Policy and Terms and Conditions.
1| Aggregation
Data aggregation is the method where raw data is gathered and expressed in a summary form for statistical analysis. For instance, raw data can be aggregated over a given time period to provide statistics such as average, minimum, maximum, sum, and count. After the data is aggregated and written as a report, you can analyse the aggregated data to gain insights about particular resources or resource groups. There are two types of data aggregation: time aggregation and spatial aggregation.
Know more here.
Download our Mobile App
2| Attribute Construction
This method helps create an efficient data mining process. In attribute construction or feature construction of data transformation, new attributes are constructed and added from the given set of attributes to help the mining process.
Know more here.
3| Discretisation
Data discretisation is the process of converting continuous data attribute values into a finite set of intervals and associating with each interval some specific data value. There are a wide variety of discretisation methods starting with naive methods such as equal-width and
equal-frequency to much more sophisticated methods such as MDLP.
Know more here.
4| Generalisation
Data Generalisation is the method of generating successive layers of summary data in an evaluational database to get a more comprehensive view of a problem or situation. Data generalisation can help in Online Analytical Processing (OLAP). OLAP is mainly used for providing quick responses to the analytical queries which are multidimensional. The method is also beneficial in the implementation of Online transaction processing (OLTP). OLTP refers to a class system designed to manage and facilitate transaction-oriented applications, especially those involved with data entry and retrieval transaction processing.
Know more here.
5| Integration
Data integration is a crucial step in data pre-processing that involves combining data residing in different sources and providing users with a unified view of these data. It includes multiple databases, data cubes or flat files and works by merging the data from various data sources. There are mainly two major approaches for data integration: tight coupling approach and loose coupling approach.
Know more here.
6| Manipulation
Data manipulation is the process of changing or altering data to make it more readable and organised. Data manipulation tools help identify patterns in the data and transform it into a usable form to generate insights on financial data, customer behaviour etc.
Know more here.
7| Normalisation
Data normalisation is a method to convert the source data into another format for effective processing. The primary purpose of data normalisation is to minimise or even exclude duplicated data. It offers several advantages, such as making data mining algorithms more effective, faster data extraction, etc.
Know more here.
8| Smoothing
Data smoothing is a technique for detecting trends in noisy data where the shape of the trend is unknown. The method can help identify trends in the economy, stocks, consumer sentiments etc.
Know more here.
