Data Transformation

For purposes of additional processing, analysis, or integration, data transformation entails changing data from one format to another. Data management and data integration both depend on the data transformation process. Similarly, businesses may enhance their data-driven decision-making by enhancing the efficiency of their data management and integration procedures. 

However, the data transformation process must keep up as more businesses use cloud-based data storage (67% of organisational infrastructure today, according to IDC). As a result, a lot of businesses are looking for alternative data integration techniques and data transformation solutions that may assist boost data quality, readability, and organisational efficiency across the board.

In this blog, I’ll talk about new data transformation technologies as well as the data transformation process and how it fits into the larger data integration procedures. 

Data transformation advantages

In general, data transformation enables organisations to take unstructured or structured raw data and convert it for use in integration, analysis, and visualisation. Data transformation is advantageous to all teams within an organisation since poor-quality, unmanaged data may have a detrimental influence on all areas of business operations. The following are some additional advantages of data transformation: 

improved data handling and organisation 

increased accessibility for users and computers

improved data quality, less mistakes

speedier data processing and more application compatibility

Integration of data

Take a step back and consider the data integration process before looking at the many approaches to change data. Data integration transforms many types of source data into integrated data by cleaning, transforming, analysing, loading, etc. the data. As a result, it is clear that data integration is only a subset of data transformation. 

Extraction, transformation, cleaning, and loading are all parts of the overall data integration process. Four distinct data integration processes—batch, ETL, ELT, and real-time integration—have been developed as a result of the combination and reorganisation of these procedures throughout time by data scientists.

integration of batches

Batch data integration is another popular technique, which entails transferring groups of previously stored data via further transformation and loading procedures. Internal databases, substantial volumes of data, and non-time-sensitive data are the key applications for this technique. 

Integration of ETL

ETL data processing uses extraction, transformation, and loading to integrate data, much to ELT. The most popular type of data integration, known as ETL integration, makes use of batch integration techniques.

Integrating in real time

Real-time integration, one of the more modern techniques for integrating data, analyses and transforms data as it is extracted and collected. This methodology is useful for data processing that needs near-instant utilisation and makes use of CDC (Change Data Capture) methods among others. 

The same ideas that are used in data integration have also been used to the smaller integration processes, such data transformation. More precisely, real-time integration of cloud technology with batch data processing has been essential in creating effective data transformation processes and tools. Let’s now examine the various data transformation steps in more detail.