What is Data Transformation?

In recent years, the data analytics lifecycle has undergone significant changes in the way data engineers and analysts get their insights from data.

The role of the data engineer has evolved massively. They are no longer solely responsible for managing databases and data warehouses that store data in a structured format. They must now also learn to deal with all kinds of data types, including semi-structured and unstructured data as well as data formats like JSON, XML, Parquet, and others.

Due to the many different possible sources of data, a significant and essential part of the modern data engineering process is to transform data, which may exist in different types and formats, into a format that makes it possible to draw meaningful insights. This need led to data transformations becoming an essential part of the data engineering process.

Data transformation definition

Raw data is often messy, inconsistent, and incomplete upon ingestion, making it difficult to analyze for valuable insights. The data transformation process involves modifying, cleaning, and enriching ingested data to improve data quality, usefulness, and accessibility.

By transforming data, data engineers and analysts can prepare it for further analysis, modeling, and visualization, ultimately leading to better decision-making and improved business outcomes. The ever-evolving role of a data engineer means that there are now also new  methods for transforming data.

Traditionally, data transformation would happen right after the data is extracted or loaded from a source, ensuring that it lands in a clean and accurate format in a target destination, which would typically be a data warehouse or data lake. This process is known as the Extract, Transform, Load (ETL) process.

However, recently there has been a shift from traditional ETL to extracting the data first, then loading it directly to a data warehouse, where all the transformations can occur. This Extract, Load Transform workflow is known as ELT. This shift is occurring for different reasons. The primary driver is that data warehousing solutions are now available as highly scalable cloud computing resources that have become accessible to many companies regardless of their maturity level. Another reason is that ELT streamlines the process in a way that reduces resource contention on data sources, making it faster to get clean, transformed data in the cloud data warehouse. Lastly, as storage and compute costs have decreased, many data engineers appreciate the ability to store a copy of the untransformed data, giving them the option to augment or re-run transformations on the original data as the needs of the business change.

Data transformation process

Like any stage in the data analysis process, data transformation involves multiple steps and is one of the areas that requires the most pre-planning to get right. Data engineers and analysts typically follow these steps to successfully get their data in the form it needs to be in.

1. Assess quality and structure: this is the data discovery part that is often used in the early stages of a data analysis project. This often involves querying and exploring datasets using built in data profiling tools as well as sampling and visualizing data to gain a better understanding of the data ingested from various data sources and how it can be used. Ultimately, this analysis reveals how the data needs to be transformed.
2. Plan the transformation: Once the quality and structure of the data have been assessed, a plan for the transformation can be developed. This involves identifying the specific steps that need to be taken to transform the data, such as cleaning, normalizing, and aggregating. This step can be used to build out data models that can be reused consistently across different transformation projects.
3. Data mapping and integration: there are certain scenarios where data would need to be moved from a specific source system that has an entirely different type and format from the target destination. For example, data integration might require data from a customer relationship management (CRM) system like Salesforce to be loaded into a specific schema format in a data warehouse. Different ETL/ELT tools exist today to ensure this mapping happens automatically.
4. Implement the transformations: The next step is to actually implement the transformation plan. This could involve using software tools to clean and manipulate the data or writing code and scripting to perform more complex transformations.
5. Validate and repeat as necessary: Depending on the complexity of the transformation and the quality of the original data, it may be necessary to repeat some or all of the steps in the process. This could involve going back and re-cleaning the data, adjusting the transformation plan, or re-validating the transformed data.

Use the data: Once the data has been transformed to an appropriate form, it is then  ready to be analyzed using business intelligence tools or fed into machine learning algorithms for predictive analytics use cases.

Why Transform Data?

When data is collected from different sources, it may be in different formats or have different structures. Data transformation can help to combine data from different sources into a unified dataset that is consistent and usable.

Data may also lack compatibility with the intended destination, which could be a database or software application. Data transformation can help to convert data into a usable format for the intended purpose and also be used to add additional information or context to the data, such as merging data with other data sources or appending metadata to make it more useful and provide additional insight to the use case at hand.

Without data transformation, data management would be much more difficult. Data can become disorganized, leading to wasted time and effort from data engineers, who should instead focus on ensuring that the data is secure, reliable, and optimized to meet operational standards.

Challenges of data transformation

Like any complex process, data transformation may also come with a set of challenges that make it harder for data analysts and engineers to follow best practices for transforming data:

• Data transformation can be expensive and resource-intensive. Specifically, dealing with big data (extremely large data sets) requires a different set of tools and skills. As data sets increase in size, data engineers are required to learn and understand new data transformation methodologies that allow them to manage data efficiently while taking into account data scalability.
• Often, in order to execute more complex tasks and build efficient data pipelines, especially for machine learning use cases, it's necessary to use Python for automation and data transformation. This means that many data engineers are no longer only required to only know SQL, but often additional languages, database functionalities, and data formats.
• Lacking a clear plan or contextual knowledge of the industry can make writing extensible data transformations difficult. Different industries have different ways of managing data. Data analysts and engineers should have a clear and deep understanding of the domain in which the data operates in order to avoid errors during data transformation.
• Data security and privacy: Data transformation may involve sensitive or confidential data, so it's important to ensure appropriate security and privacy measures are in place to protect the data.

Iterative process: Data transformation is often an iterative process, which means that multiple rounds of data aggregation, cleaning, and normalization may be required. It’s important to make sure the right automation and data transformation tools are used to prevent human error as much as possible.

Conclusion

Whether you are a data analyst, data engineer, or data scientist, data transformation is a necessary step for obtaining high-quality data and setting up projects for success. The data transformation process can be complex and time-consuming, and it requires careful planning and execution to ensure that data is accurate, consistent, and in a usable format. As the volume and complexity of data continue to grow, the importance of data transformation will only continue to increase, making it a key area of focus for data professionals and organizations alike.