Data transformation is an essential process that involves converting data from one format to another. The purpose is to transform a source system’s data into a specific configuration of a destination device or system. Different types of data transformation are aggregation, integration, manipulation, normalization, smoothing, generalization, and discretization.
Companies used different methods to transform data from one format to another. Each technique or technique comes with its own pros and cons. Therefore, it is crucial to choose a tool that best fits your needs and transforms data efficiently and quickly. The most common methods are using low code and SQL. Which one is more effective and faster? In today’s article, we will answer this question. Read on!
Low Code
Low Code is a highly effective software development approach that involves minimal coding to deliver applications faster. This approach can minimize coding for your data transformation and related operations. Not only does low code enable data teams to skip hand-coding, but it also streamlines and speeds up the transformation process.
For example, low code applications are powerful tools designed with intuitive user interfaces. You can use these tools without having extensive knowledge of computer languages. Besides, low code tools are fast and play a crucial role in data transformation using well-designed and minimalistic interfaces.
Moreover, tools developed for data transformation are highly interactive, scalable, and customizable, allowing you to create purpose-specific transformation applications. Developers use different APIs to create low code apps for data transformation.
SQL
Many data scientists and engineers use SQL to perform data analysis. Likewise, they use SQL tools and methods to perform data transformation effectively and quickly. However, this requires creating a clean dataset and finding patterns in data available for transformation. Remember, this requires coding skills and knowledge of structured query language.
Although SQL can expedite data transformation operations, including cleaning and removing duplicate data from query outputs, the most common problems are missing indexes, too many indexes, wrong indexes, and lack of maintenance. These issues can slow down your data transformation operations, affecting your company’s bottom line.
Although SQL can help transform your data and make it organized in a better manner, it sometimes causes null values, duplicates, and incompatible formats if not used correctly. On the other hand, low code applications have built-in features to properly format, validate, and improve data quality. It also protects against unexpected duplicates and incorrect indexing.
Final Words
Data scientists, engineers, and developers believe that low code application for data transformation is 60% faster than traditional methods, including SQL. Low code platforms alleviate pressure on your teams, allowing them to use minimum coding to get the job done adequately.
However, data engineers need to develop state-of-the-art models with a highly interactive user interface. Developing such tools is an excellent way to optimize your data transformation tasks and operations.
Your team no longer needs to search for lines of code. Instead, they use the drag-and-drop feature of a low code application with minimal coding to achieve the objective and improve the company’s bottom line. Until Next Time.
