Data Transformation with Web Scraper.

Have you ever been in a situation when you have two or more data sets of completely different structures? So different that it is impossible to analyze, manage, or integrate. It sounds like every professional’s worst nightmare.

Living in a data-driven world, "big data" is the most accurate word for describing the amount of information that organizations and businesses face every day. Unfortunately, the majority of raw data is unstructured and can be of various types, making different data sets hard or near impossible to compare or integrate.

This is where the topic of data transformation surfaces. It is a process through which differently structured data sets are reconstructed, making two or more data sets compatible for further analysis.

Nowadays, in the global marketplace, good data is the fuel that runs the modern and dynamic business analysis. When extracting information, data that contains non-standard characters, symbols, out-of-date information ruins the quality or consistency of it. Unstructured data and databases become one of the biggest burdens that further slow down other operations in a business. Therefore, such processes as:

1. summarizing;
2. filtering; 
3. merging; 
4. enriching;
5. joining; 

are applied to transform data based on the desired final output structure.

For businesses that have various databases attached to different structures, data transformation is a mandatory step. For example, if a company has to assess an overall sales report that includes various of its locations sales data, which does not have a unified structure, data analysis can be very time-consuming or near impossible.Therefore, the data conversion from the various regions to a unified format saves time and ensures the precision of data for further analysis.

With the high increase of data, a variety of tools and technologies have been developed that would match the data transformation needs of anyone. The choice can be based on numerous reasons, like the data types, structures, formats, volumes, and others.

Such procedures as ETL (extract, transform, load) have been created and mainly used by organizations that have on-premises data warehouses ensuring that data can be pulled from one database and placed into a different one by data extraction, transformation, and loading. These tools often are quite costly, slow, and, traditionally would be implemented by outsourcing a developer which would ensure the ETL process through scripting, hand-written codes in SQL or Python.

For scraped data, generally, one would extract the data, and manually delete columns, words, symbols, etc., which is a quite time-consuming and tiring data transformation process and does not guarantee the sure success of not missing a data cell.

However, for Cloud Scraper users, the Parser works as a form of data transformation tool for the scraped data. Allowing the user to delete or replace words, symbols, strings, eliminate whitespaces, remove unnecessary columns, etc; therefore, cleansing data, and making it easier for integration or analysis. Everything with one feature.

To read more specifically about the Parser feature, how it works, visit our informative blog about the Parser feature.

Leverage your data analysis with data transformation!