Some basic information on data transformation

Posted by John Dickerson on December 29th, 2020

This article deals with some of the basic information on data transformation. If you are looking for Data transformation, or if you are looking for Database maintenance automation, consider Data Logue. 

What is data transformation? 

If you are in need of analyzing information for the most optimal results, you will require structured and accessible data for best results. With the help of data transformation, what you can do is that you can enable organizations to alter and change the structure and also the format of raw data as required. As an entrepreneurial organization, it is important that you learn to transform data effectively for analytical purposes. 

So what exactly is Data transformation? How do you define data transformation? And how does it come in handy? Data transformation can be defined as the process by which the format, structure and value of data can be altered or changed. As far as data analytics projects are concerned, data is transformed at two different stages of the data pipeline. Some organizations use the ETL process which stands for extract, transform, and load. These are the companies which make use of on-premises data warehouses. Here, the data transformation process is the middle step.

In the contemporary world, most of the organizations make use of cloud-based data warehouses. With the help of it one can scale compute and also helps in storage of resources and it is done with latency measured in seconds or minutes. 

This property or characteristic of applicability of the cloud platform is actually a boon for the organizations as it enables them to skip preload transformations and at the same time load raw data into the data warehouse, and then transform it at query time. Most of the popular and crucial processes of most organizations such as data integration, data migration, data warehousing, data wrangling, etc will include some form of data transformation. 

Data transformation can take different forms. It can be constructive which will primarily involve adding, copying, and replicating data, or it can be destructive which will involve deleting fields and records. It can be aesthetic which will include standardizing salutations or street names, or it can be structural in nature which will basically involve renaming, moving, and combining columns in a database.

There is a wide array of benefits of Transforming data. Basically an enterprise can reap so many benefits from the process. Here, we shall be focusing on some of the important aspects of the same. 

The transformation of data is done in order to make it better-organized. Transformed data is basically much more convenient and easier for both humans and computers to make use of it.

When you have properly formatted and also validated data it can help improve the quality of data. It can also protect the various applications from issues like null values, unexpected duplicates, incorrect indexing, etc. 

With the help of data transformation one can also facilitate compatibility between applications, systems, and types of data. 

John Dickerson is the author of this article. To Know More about Neural network data cleanse please Visit here:https://www.datalogue.io/about-us