Data Transformation is the process of changing data from one format, structure, or standard without altering the data. It's used primarily to make data available for users or apps to use, or to improve data quality. Data transformation can be divided into six types depending on the technique and the outcome desired.
What is Data Transformation?
Data Transformation is the process of changing data from one format, structure, or standard without altering the data. It's used primarily to make data available for users or apps to use, or to improve data quality.
Data transformation refers to the modification of data's format, organization, and values. When data analytics projects are being undertaken, data can be modified at two points in their data pipeline. On-premises data warehouses are often used with an ETL (extract transform, load) method, with data transformation acting as an intermediate step. Cloud-based data warehouses can increase computational and storage capacities with delay estimates of seconds or minutes.
ELT (extract-load, transform) allows organizations to load raw data directly into their data warehouses, without any preload adjustments, and then convert it when they receive a query. Data transformation can be used in many operations such as data migration, integration, and wrangling.
It is essential for any company that wants to use its data to deliver timely business insights. As the amount of data is increasing, organizations need reliable methods to use it. Data transformation is an important part of using this data because it ensures that information is consistent, safe, and easily accessible to the intended business users.
Different Types of Data Transformation
Data transformation can be divided into six types depending on the technique and the outcome desired.
1. Data Transformation Using Scripting
Scripting allows data to be extracted and transformed using code written in Python or structured question language (SQL). You can automate many processes by using scripting languages such as Python or SQL in software. They also allow data collection to be mined for information. Because scripting languages require less code than conventional programming languages, they are more efficient.
Programming languages such as Python allow one to modify multiple jobs. This allows the company's schedules and jobs to be managed. To alter the content or structure of an XML event, transformation scripts must be used before they can be written.
2. ETL tools that are on-premises
On-premise ETL software can be deployed within your company so you have complete control over its security and modification. The software allows you to perform hundreds of jobs simultaneously. Cloud software is slower than on-premise software because the data is sent first to the manufacturer's server, then it is recovered in an alternative format.
Businesses that want to protect their data and need privacy and security will love it. This includes banks and governments that hold sensitive information. These tools can be used remotely but often require third-party connectors for mobile devices.
3. Cloud-based ETL tools
Many businesses have experienced difficulties with cloud-based data ownership. Because encryption keys and data keys are stored with third-party suppliers, it will be difficult to access data in the event of an unanticipated event.
Many cloud-based ETL systems allow you to access them via mobile devices. Some even have native mobile apps. Accessibility comes with higher security risks, especially if employees access company files from their mobile devices. The public cloud hosting provider houses and manages resources using cloud-based ETL tools.
4. Data transformation: Constructive and Destructive
Constructive transformation is the process of adding, copying, and replicating data to fill in the gaps and standardize data. When data is cleaned up and made more useful through destructive transformation, it can lead to the deletion of entire records. Because programs are complex, difficult to understand, and susceptible to errors, they must be effective according to the core principle of the transformation approach for program development. This involves structuring software development as a process. However, this can lead to confusion for the economy.
5. Structural Data Transformation
Another term for structural change is a reorganization. Data can be combined, split rearranged, and even generated in bespoke data structures. To restructure a database, columns can be moved or combined. Based on the way they alter a dataset's topology, geometry, and characteristics, four types of structural transformations can be classified.
Depending on the final and initial target data, transformation can be either simple or complex. As part of structural data transformation, columns can be renamed or moved. Data transformation is at its core, the process of turning unusable data into useful data.
6. Data Transformation for Aesthetic Purposes
A part of aesthetic evolution is stylistic modifications such as standardizing street names and other values. But, structural restructuring includes moving or combining columns. The way data aesthetics are viewed has changed with the new aesthetic ideas that have emerged in every era.
The transformation standardizes data to meet specifications and parameters. Transforming data can help organizations learn a lot about their operational and informational tasks. Because of the huge amounts of data they have to manage every day, data transformation is a vital tool for businesses.
Data transformation is an integral part of any IT organization. You need interoperability in order to fully utilize the potential of your IT systems. Data transformation allows information assets to be shared across platforms and systems. Data transformation also allows for greater standardization and quality improvement of enterprise data, allowing you to use it to create exponential value.