Today business and industry leaders are heavily reliant on data and data scientists for making data-driven decisions to ensure maximum business growth. In order to get meaningful insights out of data, it is important to make the necessary changes and alterations to it. One such possible way is data transformation which enables organizations and businesses to change data to meet crucial business objectives.
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What do you mean by data transformation?
Data transformation involves altering data from one format or type to another. It includes altering values, such as financial metrics, within a given data set. It is an important component of huge data processes and includes data management and data integration. Data transformation has a significant role to play in the data science field and comes in four main types as the following
- Constructive transformation, in other words, adding, replicating, and copying data
- The destructive transformation, which includes deleting data
- Aesthetic data transformation, which means changing data to make it visually attractive and appealing
- Structural data transformation reformat such as relocation and moving of data
Based on the earlier and final data status, transformation can be complex or very simple. Due to the difference in organizational needs, the complexity of data, and operational requirements, the technologies and tools that data science professionals have to use for performing data transformation can vary.
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How is data transformation used in data science?
The process of data transformation occurs in one or two data pipeline stages. A data pipeline is a pathway so that data can follow a particular location and reach its destination. There are three main stages in the process. They are
- Extraction
- Transformation
- Loading
All these stages can be performed in a distinct order so that every stage can offer unique advantages. The below system process showcases how data science professionals and data scientists are using data transformation in the field of data science.
ETL systems
ETL stands for extract, transformation, and loading systems. In this system, a data scientist is required to extract data from different sources. After data collection, it has to undergo some essential transformations like adjusting the data format or performing important calculations. At the final stage, the transformed data is loaded into its transformations. A prime benefit of using an ETL system for data transformation is that it enables data scientists to ensure faster data analysis. Companies who are concerned about data compliance prefer ETL systems since this procedure enables organizations to make adequate changes before converting data into the data warehouse.
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ELT systems
ELT system stands for extracting, loading, and transforming data, and in this system, data takes place after the data reaches the final destination. In this method, a data warehousing system that is based on a cloud platform is used for conducting the transformation, and no separate staging area is relied on. A prime benefit of using an ELT system for data transformation is that it enables data scientists to save a good amount of information even if they have not modified the data in the first instance. It also enables them to access data quickly without wasting time. ELT system generally incurs low maintenance costs, and there is no need to build complex ETL process to gain new information
What are the advantages of using data transformation in data science?
There are a plethora of data benefits that you can get by transforming data, such as the following.
- Broader data application
Data transfer enables data scientists to use and apply data more. For instance, companies collecting data about their customers in a single application may require transforming the customer data. Therefore data transformation enables businesses and organizations to make broader use and application of data and enables data scientists to make data accessible for different uses.
- Easy use of data
Data transformation enables people, as well as computing devices, to use data easily. For instance, data makes it easy for people to read data by altering its format or layout. When different types of data are standardized, organizations can manage huge amounts of data effectively. It also makes it easy to access and use transformed data. Data transform enables users to get the right type of data that they require.
- Better quality of data
Data scientists and analysts must monitor data quality to ensure that the organization or business makes a better data-driven decision based on that data. If errors or mistakes are present within the data, then employees or business leaders will miss out on important information that will not fetch successful outcomes. With the help of greater transformation, organizations can remove inconsistencies in their operations without missing data to ensure the accuracy and quality of data that can be analyzed to make better decision making.
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What are the data transformation tools that data scientists handle?
The data transformation procedure involves using different technologies and tools, such as the following.
Databases
A database is a well-organized form of collected data. Today advanced databases have electronic record information in computing devices. Databases form an important part of the whole process, with the help of which data scientists can store data before or after making data transform.
Scripting languages
Data engineers use scripting languages in data science to perform successfully. Commonly used scripting languages that are used for data transformation are SQL and Python. Data analysts and engineers must work with scripting languages to make alterations to data or information.
Automated Transformation Technology
Automated transformation technology and tools are third-party software that performs the function of transformation. There is a wide array of automatic transformation technology and tools available that can meet the specific needs and requirements of an organization.
Cloud integration
To select the right type of tool, organizations consider several factors, such as the common types of data they usually work with, their budgeted range, and the common and desired features of tools.
Data scientists ensure that the particular data transformation tool they use should work seamlessly and integrate with their existing database application and system.
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Conclusion
Data transformation is essential in the field of data science and for modern business and industrial environments. Data transformation is required for everything from supply chain management to customer service. Fully study the process of organizing data and analyzing it to reveal meaningful insights that can assist in data-driven decision-making for the organization. Without data transformation, business organizations would have to rely on conventional methods, and old-fashioned data analysis approaches like charts and manually prepared spreadsheets with limited features and use.
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