https://www.datasciencecentral.com/data-transformation-101-process-and-new-technologies/
Data transformation involves converting data from one format into another for further processing, analysis, or integration. The data transformation process is an integral component of data management and data integration. Likewise, companies can improve their data-driven decision-making by streamlining their data management and integration processes through data transformation.
However, as more and more companies adopt cloud-based data storage (IDC reports that today 67% of enterprise infrastructure is cloud-based), the data transformation process must follow suit. Consequently, many companies are searching for alternative data integration processes and data transformation tools that help improve the data quality, readability, and organization company-wide.
In this article, I will explore the data transformation process, how it contributes to the broader processes of data integration, as well as new data transformation technologies.
From a general perspective, data transformation helps businesses take raw data (structured or unstructured) and transform it for further processing, including analysis, integration, and visualization. All teams within a company’s structure benefit from data transformation, as low-quality unmanaged data can negatively impact all facets of business operations. Some additional benefits of data transformation include:
Before examining the various ways to transform data, it is important to take a step back and look at the data integration process. Data integration processes multiple types of source data into integrated data, during which the data undergoes cleaning, transformation, analysis, loading, etc. With that, we can see that data transformation is simply a subset of data integration.
Data integration as a whole involves extraction, transformation, cleaning, and loading. Over time, data scientists have combined and rearranged these steps, consequently creating four data integration processes: batch, ETL, ELT, and real-time integration.
Another common method is batch data integration, which involves moving batches of stored data through further transformation and loading processes. This method is mainly used for internal databases, large amounts of data, and data that is not time-sensitive.
Similar to ELT, ETL data processing involves data integration through extraction, transformation, and loading. ETL integration is the most common form of data integration and utilizes batch integration techniques.
ELT data processing involves data integration through extraction, loading, and transformation. Similar to real-time integration, ELT applies open-source tools and cloud technology, making this method best for organizations that need to transform massive amounts of data at a relatively quick pace.