Data Transformation Types

data transformation in batches

Data are transformed in groups over time as part of batch data transformation, sometimes referred to as bulk data transformation. Scripted languages like Python and SQL are used manually for traditional batch data transformation, which is today viewed as fairly antiquated. 

Batch transformation more particularly refers to ETL data integration, where data is first stored in one area before being converted and transported incrementally over time. A number of data integration activities, including web application integration, data warehousing, and data virtualization, benefit greatly from batch data transformation. The ideas and procedures used in batch data transformation might help other data integration processes by streamlining their operations.

Transforming data interactively

End-users of such data are seeking more adaptable ways to change data as more businesses migrate to cloud-based platforms; IBM even states that 81% of enterprises employ various cloud-based systems. Similar ideas are used in real-time integration and ELT processing as well as interactive data transformation, sometimes known as real-time data transformation. 

Batch data transformation has been expanded to include interactive data transformation. The steps aren’t always sequential, though. Interactive data transformation, which uses previously created and examined code to find outliers, trends, and problems in the data, is gaining popularity for its user-friendly visual interface. This data is then transmitted to a graphical user interface so that human end users may easily see trends, patterns, and other aspects of the data.

languages for data transformation

Developers can use a range of transformation languages to convert formal language content into a more usable and understandable output text in addition to other sorts of data transformation. Macro languages, model transformation languages, low-level languages, and XML transformation languages are the four primary categories of data transformation languages. 

Data transformation frequently employs the following codes: ATL, AWK, identity transform, QVT, TXL, XQuery, and XSLT. Data scientists must ultimately take into account the source of the data, the type of data being changed, and the project’s goal before choosing a transformation method and language.

The method of data transformation

I can now look at the more specific processes in data transformation itself after discussing the wider picture of how data transformation fits into the greater picture of data integration. First of all, it’s crucial to remember that, although data may be transformed manually, businesses now mostly or entirely rely on data transformation technologies. The same procedures are involved in both human and automated data transformation. 

1. Data exploration and analysis

Data discovery and data processing are steps in the data transformation process that come initially. For specialised market insights and corporate intelligence, the processes of data discovery and data parsing entail gathering, combining, and reorganising data.

2. Data translation and mapping

You can execute data mapping and translation once you’ve profiled your data and determined how you want to alter it. Data aggregation, filtering, and mapping are all terms used to describe the process of preparing data for subsequent processing. For instance, in batch transformation, this phase would assist in batch-wise data filtering and sorting to ensure seamless operation of executable code.

3. Creating programmes and codes

In order to generate code for data programming, programmers will use executable coding languages like SQL, Python, R, or other executable instructions. Developers are currently closely collaborating with transformation technologies, also referred to as code generators. Code generators are a favourite among developers since they give them a visual design environment and work on several platforms. 

4. Data transformation

The code may now be tested against your data because it has been developed. This stage, often referred to as code execution, is the last one before the data is used by people as end users.

5. Examining the information 

The data has now been processed by the code and is ready for inspection. This stage, which is comparable to a quality assurance check, verifies that the data has been converted correctly. It is crucial to keep in mind that this process is iterative, and that the developers must be informed of any flaws discovered in the translated data before they can be corrected.