Data Transformation: Benefits, Types, and Processes

Types of data transformation

Batch data transformation

Batch data transformation, also known as bulk data transformation, involves transforming data in groups over a period of time. Traditional batch data transformation involves manual execution with scripted languages such as SQL and Python and is now seen as somewhat outdated. 

More specifically, batch transformation involves ETL data integration, in which the data is stored in one location and then transformed and moved in smaller batches over time. It is important to note the significance of batch data transformation on many data integration processes, such as web application integration, data warehousing, and data virtualization. When applied to other data integration processes, the concepts and logistics within batch data transformation can improve the overall integration process. 

Interactive data transformation

As many companies turn to cloud-based systems, IBM even reports that 81% of companies use multiple cloud-based systems, end-users of said data are also looking for more versatile methods to transform data. Interactive data transformation, also referred to as real-time data transformation uses similar concepts seen in real-time integration and ELT processing. 

Interactive data transformation is an expansion of batch transformation. However, the steps are not necessarily linear. Gaining traction for its accessible end-user visual interface, interactive data transformation takes previously generated and inspected code to identify outliers, patterns, and errors within the data. It then sends this information to a graphical user interface for human end-users to quickly visualize trends, patterns, and more, within the data. 

Data transformation languages

In addition to the various types of data transformation, developers can also utilize a variety of transformation languages to transform formal language text into a more useful and readable output text. There are four main types of data transformation languages: macro languages, model transformation languages, low-level languages, and XML transformation languages. 

The most commonly used codes in data transformation include ATL, AWK, identity transform, QVT, TXL, XQuery, and XSLT. Ultimately, before deciding what transformation method and language to use, data scientists must consider the source of the data, the type of data being transformed, and the project’s objective.

The data transformation process

Now that we’ve covered the bigger picture of how data transformation fits into the larger picture of data integration, we can examine the more granular steps in data transformation itself. Firstly, it is important to note that while it's possible to transform data manually, today, companies rely on data transformation tools to partially or fully transform their data. Either way, manual and automated data transformation involves the same steps detailed below. 

1. Data discovery and parsing

The first step in the data transformation process involves data discovery and data parsing. Data discovery and data parsing are processes that involve collecting data, consolidating data, and reorganizing data for specific market insights and business intelligence. At Coresignal, we can offer you parsed, ready-to-use data.

2. Data mapping and translation

Once you have profiled your data and decided how you want to transform your data, you can perform data mapping and translation. Data mapping and translation refer to the process of mapping, aggregating, and filtering said data so it can be further processed. For example, in batch transformation, this step would help filter and sort the data in batches so executable code can run smoothly.

3. Programming and code creation

The data programming involves code generation, in which developers will work with executable coding languages, such as SQL, Python, R, or other executable instructions. During this stage, developers are working closely with transformation technologies, also known as code generators. Code generators provide developers with a visual design atmosphere and can run on multiple platforms, making them a favorite among developers. 

4. Transforming the data

Now that the code is developed, it can be run against your data. Also known as code execution, this step is the last stage the data passes through before reaching human end-users.

5. Reviewing the data 

Once the code executes the data, it is now ready for review. Similar to a quality assurance check, the purpose of this step is to make sure the data has been transformed properly. It is important to note that this step is iterative, in that end-users of this data are responsible for reporting any errors they found in transformed data to the developers, so edits to the code can be made. 

ETL vs. ELT

The recent advancements in big data have required businesses to look elsewhere when storing, processing, and analyzing their data. Moreso, the increasing variety in data sources has also contributed to the strain being placed on data warehouses. Particularly, while companies acquire powerful raw data from data types such as firmographic data, employee data, and social media data, these same data types typically export very large file sizes. Consequently, companies have been searching for alternative methods. 

This search has greatly impacted data integration processes, specifically data transformation. That is, companies have been transitioning from traditional data integration processes, such as ETL methods, to cloud-based integration processes, such as ELT and real-time integration. 

In the past, many companies have relied on local servers for data storage, making ETL integration the preferred method. However, due to the significant increase in digital communication and business operations in 2020, global data creation is now modeled at a CAGR of 23%, according to Businesswire. Subsequently, the upward trend in global data creation has put a strain on local servers and data storage, and many businesses are looking elsewhere for cloud-based solutions. 

What is data transformation in ETL? 

ETL, which stands for extraction, transformation, and loading, is a data integration process that involves extracting data from various external sources, often from third-party data providers, transforming the data into the appropriate structure, and then loading that data into a company’s database. The ETL process is considered the most common integration process compared to ELT, ETM, and EMM transformation processes.

Data transformation within ETL occurs in the transformation step; however, it is closely linked to the extraction and loading stages. Traditionally, data transformation within the ETL method utilizes batch transformation with linear steps, including discovery, mapping, programming, code execution, and data review. 

Summary

As businesses collect an increasing volume of data, many are forced to find data storage and processing solutions that can handle massive amounts of data with limited money and resources. Similarly, companies are also looking for data transformation solutions that can meet the current needs and industry standards. Companies are recognizing the future of data transformation and shifting towards utilizing cloud-based technology in processes such as ELT integration and interactive data transformation. 

This article was originally published on Data Science Central.