Use of a Web Scraper for Data Transformation

No Comments

Have you ever experienced a scenario in which you needed to work with two or more data sets that had completely distinct structures? Because of the significant differences, it cannot assess, manage, or integrate it. It sounds like the worst nightmare that could ever befall a professional.

Because we live in a world that is driven by data, the phrase “big data” is the most accurate way to describe the amount of information that companies and organizations must deal with on a daily basis. Unfortunately, most raw data is unstructured and can take various forms. Because of this, it can be challenging or nearly impossible to compare or integrate disparate data sets.

The subject of transforming data comes up at this point in the discussion. It is a procedure that involves reconstructing data sets that have distinct structures, with the goal of making two or more data sets compatible with one another for the sake of further study.

In the present day, modern and dynamic business analysis is powered by accurate data, which is the fuel that operates the global marketplace. When information is extracted, the quality or consistency of the information is ruined if it comprises data that contains data that contains non-standard characters, symbols, or information that is out of date. One of the most significant burdens that further slows down other operations in an organization is the presence of databases and data that are not organized. This results in the following processes:

In order to change the data so that it conforms to the intended final output structure, operations such as summarizing, filtering, merging, enriching, and joining are carried out.

The phase of data transformation is obligatory for companies that have many databases that are connected to a variety of organizational structures. For instance, if a corporation needs to analyze an overall sales report that includes sales data from several of its sites but does not have a consistent structure, the process of data analysis may be extremely time-consuming or nearly impossible to complete. As a result, converting the data from all of the different locations into a single format not only saves time but also guarantees the accuracy of the data for any further analyses.

Because of the dramatic increase in the amount of data, a wide array of tools and technologies have been developed to meet the requirements for data transformation that can be posed by any individual. The decision could be influenced by a wide variety of factors, including the volumes, data kinds, structures, and formats, amongst others.

In order to ensure that data can be extracted from one database and placed into a different one by means of data extraction, transformation, and loading, specific procedures such as ETL (extract, transform, load) have been developed. They are primarily utilized by businesses that have on-premises data warehouses. These procedures ensure that data can be pulled from the original database. These tools are typically fairly pricey and incredibly slow, and, traditionally, their implementation would involve the outsourcing of a developer who would ensure the ETL process by scripting hand-written scripts in SQL or Python.

When working with scraped data, the standard procedure involves extracting the data and then manually removing columns, words, symbols, and other elements. This is a data transformation process that is highly time-consuming and taxing, and it does not guarantee that any data cells will be overlooked.

On the other hand, users of Cloud Scraper will find that the Parser functions as a form of data transformation tool for the data that was scraped. Giving the user the ability to delete or change words, symbols, strings, erase whitespaces, remove extraneous columns, and so on is an effective way to clean data and make it simpler to integrate or analyze. Everything is characterized by a single trait.

Visit our enlightening blog post about the Parser feature if you want to get more specifics about how it operates and how the Parser feature itself works.

Utilize data transformation to get the most out of your data analysis!

Frequently asked questions:

What can web scraping provide as a means for?

the outcome of a picture Use of a Web Scraper to Change Data

Web scraping is the process of using bots to collect data and content from a website. In contrast to screen scraping, which only scrapes pixels shown onscreen, web scraping collects the underlying HTML code and, with it, data stored in a database. The full webpage can then be copied by the scraper and placed somewhere else.

Does ETL include web scraping?

One type of ETL is web scraping, where you extract data from a website, change it to meet your desired format, and then load it into a CSV file. You must have a rudimentary understanding of HTML, the building block of every web page you see online, in order to extract data from the web.

Can web scraping be used to obtain data?

An automated technique called web scraping is used to retrieve copious volumes of data from websites. On the internet, unstructured data can be found. These unstructured data can be collected and stored with the help of web scraping. Website scraping can be done in a number of ways, such as by using internet services, APIs, or even your own applications.

About us and this blog

We are a digital marketing company with a focus on helping our customers achieve great results across several key areas.

Request a free quote

We offer professional SEO services that help websites increase their organic search score drastically in order to compete for the highest rankings even when it comes to highly competitive keywords.

Subscribe to our newsletter!

More from our blog

See all posts