What is data transformation?
Data transformation is the process of converting data from one form to another. The conversion could be changing the structure, format, or values of data. Data transformation is typically performed with the help of data preparation software.
Additionally, data migration, data integration, data warehousing, and data wrangling will all involve data transformation. Data transformation is also the middle step of the ETL (extract, transform, load) process, which is performed by data warehouse software.
Typically, data engineers, data scientists, and data analysts use domain-specific languages such as SQL or scripting languages such as Python to transform data. Organizations may also choose to use ETL tools, which can automate the data transformation process.
With enterprises using big data analytics software to make sense of big data, the process of data transformation is even more crucial. This is because there’s a continually increasing number of devices, websites, and applications generating significant amounts of data, which means there will be data compatibility issues.
Data transformation empowers organizations to make use of data, irrespective of its source, by converting it into a format that can be easily stored and analyzed for valuable insights.
Types of data transformation
There are different types of data transformation as listed below:
- Structural: Moving, renaming, and combining columns in a database.
- Constructive: Adding, copying, and replicating data.
- Destructive: Deleting records and fields.
- Aesthetic: Systemizing salutations.
Benefits of data transformation
Data transformation enhances interoperability between different applications and ensures higher scalability and performance for analytical databases and data frames. The following are some of the common benefits of data transformation:
- Improved data quality as missing values and inconsistencies are eliminated
- Increased use of data as it is standardized
- Enhanced data management as data transformation can refine the metadata
- Improved compatibility between systems and applications
- Improved query speeds as data is easily retrievable
Basic elements of data transformation
The primary purpose of data transformation is to transform data into a usable format. As mentioned earlier, transformation is part of the ETL process, which is a data transformation process that extracts and transforms data from multiple sources and loads it into a data warehouse or other target system.
Typically, data goes through the data cleaning process before data transformation to account for missing values or inconsistencies. Data cleaning can be performed using data quality software. Post the cleaning process, the data is subjected to the transformation process.
The following are some of the key steps involved in the data transformation process. More steps can be added or existing steps can be removed based on the complexity of the transformation.
- Data discovery: In this first step of data transformation, data is profiled with the help of data profiling tools or manual profiling scripts. This helps to better understand the characteristics and structure of data, which helps decide how it should be transformed.
- Data mapping: This step involves defining how each field is mapped, joined, aggregated, modified, or filtered to generate the final output. It’s typically performed with the help of data mapping software. Data mapping is usually the most time-consuming and expensive step in the data transformation process.
- Data extraction: In this step, data is extracted from its original source. As mentioned above, the sources can vary significantly and may also include structured ones.
- Code generation: This step involves generating executable code in languages such as Python, R, or SQL. This executable code will transform data based on the defined data mapping rules.
- Code execution: In this step, the generated code is executed on the data to convert it into the desired format.
- Data review: In this final step of data transformation, the output data is reviewed to check whether it meets the transformation requirements. This step is usually performed by the end user of data or the business user. Anomalies or errors found in this step are communicated to the data analyst or developer.
Data transformation best practices
The following are some of the best practices to keep in mind when performing data transformation:
- Design the target format
- Profile the data to understand what state the raw data is available in—this will help users understand the amount of work required to make it ready for the transformation
- Clean data before transforming to increase the quality of the final transformed data
- Use ETL tools
- Use prebuilt SQL to expedite analytics
- Engage end users continually to understand the extent to which the target users accept and utilize the transformed data
- Audit the data transformation process to quickly identify the source of the problem if any complication occurs
Amal Joby
Amal is a Research Analyst at G2 researching the cybersecurity, blockchain, and machine learning space. He's fascinated by the human mind and hopes to decipher it in its entirety one day. In his free time, you can find him reading books, obsessing over sci-fi movies, or fighting the urge to have a slice of pizza.
