Data transformation is the process of converting, cleaning, and organising data into a format that is usable and that can be analysed to aid in decision making and to propel the growth of an organisation.
Data transformation is used to adapt source data to the format required by the target system. There are two possible points along the data pipeline where this can occur. Data is first extracted, transformed, and loaded in on-premises data storage environments. The data transformation occurs during the intermediate ‘transform’ step.
Data transformation is an integral part of many workflows, such as data integration, data migration, data warehousing, and data wrangling. Data transformation can be either constructive (resulting in additional data creation via addition, duplication, or replication) or destructive.
- This operation is destructive because it overwrites or deletes data that already exists in the database.
- Aesthetic, wherein some values are standardised, or
- Modifications to the underlying structure, such as adding, removing, or rearranging columns.
At its core, the process of transforming raw data into a usable format entails getting rid of duplicates, changing the data types, and improving the dataset. Defining the structure, mapping the data, extracting the data from the source system, performing the transformations, and finally storing the transformed data in the appropriate dataset are all steps involved in the process of transforming data. After that, the data is made available, protected, and more usable, allowing for a plethora of new uses. To make sure their data is compatible with the other types of information they are combining it with or migrating it into a dataset, businesses perform data transformation. When data is transformed, businesses gain novel insights into their informational and operational procedures.
Where Can You Put Data Transformation to Use?
The process of data transformation consists of three stages: the extraction of data from a source, the transformation of that data into a usable format, and the delivery of the transformed data to the system that will make use of it. During the “extraction” stage, data is gathered from various locations and added to a centralised database. This results in data that is typically in its unprocessed, raw form. The extracted data needs to be processed through a series of steps that will convert it into the desired format before it can be put to good use. In some cases, the data will need to be cleaned up before the transformation can occur. By finishing this process, any missing data or inconsistencies in the dataset will be fixed. The data transformation procedure entails five distinct stages.
The first step is to recognise and understand the data in its native format with the help of data profiling tools. The first step is to identify all of the data sources and types that need to be transformed. This helps clarify how the data should be transformed to meet the requirements of the target format.
It is during this mapping stage that the transformation strategy is developed. This involves mapping the data to gain a fundamental understanding of the way in which individual fields would be modified, joined, or aggregated, and then determining the existing structure and the transformation that will be necessary as a result of it.
The Creation of Code
Using the platform or tool for data transformation, the necessary code to carry out the transformation is written. This is an essential part of the process.
Putting to death As a result of the code, the information is converted into the desired format. The information is gathered from its original location, which could be anything from a database to a telemetry feed to a set of log files. Following the completion of the mapping stage, the data is transformed in accordance with the predetermined plans, which may include aggregation, format conversion, or merging. After processing, the data is sent to its final location, which could be a data warehouse or a dataset.