Data Transformation is the process of converting data from one format to another format which is useful to business users for decision making. In Data management data transformation is a key component of data integration and pre-processing. Data transformation involves activities such as data discovery, data mapping, filtering, combining data, applying different aggregation functions and business logic among other specific tasks. Data transformation can be simple or complex depending on the business requirement, type of data and the transformation rules to be applied. There are different tools that automates this process such as ETL/ELT based tools, Programming languages (SQL, Python etc.). In this post we will look at data transformation as a pre-processing process, its benefits, types of data transformation and the process of data transformation.
Importance of Data Transformation
- Improved data quality. Data transformation aims to increase quality of data as required by business use-case.
- Efficient data management. Data can be well organized and stored when transformed. Redundancy is eliminated during data transformation and only quality and useful data is stored.
- Increases data consistency. Data transformation standardizes data from different source systems to a common dataset with transformations defined by business rules.
- Quicker access to good data and faster decision making from the transformed data.
Types of Data Transformation
- Batch data transformation. This involves applying bulk data transformation process to data.
- Real-time data transformation. With emerging trend in real-time analytics real-time data integration and transformation is key in making the data available in real-time for analytics.
- Interactive data transformation. This involves using low-code no-code tools to perform data transformation. This technique allows users with less technical skills to perform ad-hoc transformation of their data for analysis.
Data Transformation Process
- Data Discovery. This involves extracting and profiling the data to determine its structure and inform the initial transformations required.
- Data Mapping. Data mapping defines how individual fields should be modified and which operations and aggregation rules can be applied. Different operations can be defined here such as filter, split, join, remove duplicates etc.
- Code Generation. The process of data transformation involves defining rules for manipulating the data and results in creating of code (depending on the language used e.g. SQL, Python etc.) that needs to be executed to convert the data.
- Code Execution. This is the process of running the transformation code created to produce the output dataset. The execution process is usually integrated in the transformation (ETL/ELT) tool or the data storage engine such as database.
- Data Review. This involves checking if the transformation rules have been applied successfully.
Data Transformation Tools
Data Transformation process can be expensive, resource-intensive and complex. Careful consideration needs to be taken into place when planning for this process.
- Manual data transformation. This only applies when the data is too small, the business logic is too complex to automate or not available out-of-the-box from the transformation tool.
- Programming Languages. Programming languages such as SQL, Python, Java etc. are very powerful in data transformation and allows for definition of complex and custom functions easily.
- ETL/ELT Tools. At the core of ETL/ELT tools is the transformation capabilities. Most ETL/ELT tools comes with numerous transformation functions and capabilities. Check our previous here to learn about data integration. ETL/ELT tools can be classified into real-time or batch tools.
In this post we have looked at data transformation, why it’s important, types of transformations, how to perform it and categories of tools used for data transformation. Data transformation is an important step in data integration and pre-processing process Wikipedia. To learn about data integration check our series of post here. In the previous post we covered about data integration as pre-processing process. In the next post we will look at data reduction.