ETL Data Transformation: Types and Process

If you deal with any kind of data, you’ll likely be familiar with ETL (extract, transform, load) processes. Of the three phases of ETL, transforming is the one that most people in your organization can participate in. Maybe you need to compile CRM data. Perhaps your marketing team wants to aggregate sales data with data from their recent marketing campaign, or someone in finance needs to compare sales numbers from two different databases. These are all prime use cases for data transformation. 

The more data literacy is enhanced in your company, the more employees will have to transform data into consistent, usable formats. So what exactly is this transformation phase, and how does ETL transformation benefit your company?  

What is ETL transformation?

ETL transformation is the “transform” part of the “extract, transform, load” process. The whole ETL process is designed with the end goal of collecting, preparing, and storing data successfully in a single central repository. In this context, transformation means transforming data into a usable format. ETL transformations can differ depending on the data set, the end destination system, the quality of the data, and your end goal with the data. However, transformation usually involves cleaning the data and formatting it to make it easy to store. 

Key components of ETL data transformation

While the exact ins and outs of data transformation may vary from company to company, here are some of the basic key components of data transformation in ETL: 

• Data cleaning, which involves making the data accurate and getting rid of inaccuracies.
• Data mapping, which identifies types of data you already have and maps them to corresponding fields in your target system.
• Code generation, which actually transforms the data.

Benefits of ETL transformation

ETL data transformation has several benefits. The ETL process helps companies understand data, which, in turn, helps them make better business decisions and gain more market insights. An advantage of ETL is that it gathers data from multiple different sources and combines them, which gives companies a holistic view of what’s happening across the organization. Most ETL processes can gather data from all your company’s data sources, including your CRM platform, ERP tools, databases, email repositories, and system logs. The more data a company has, the better it understands its customers, products, and competitors. 

Not only does ETL gather and centralize the data, but the transformation phase of the process cleans the data, getting rid of errors and duplicates so you have more accuracy. Companies can make strategic decisions based on real-time ETL data to prepare the organization for the future. 

The ETL data transformation process

Step-by-step guide to the ETL data transformation process

There are two main ETL transformation types. The first type, the more common way, is called multistage data transformation. In this process, the data is extracted from its source and then moved to a staging area where the transformations happen. Once the transformation is complete, the data can be stored in a warehouse. Alternatively, you can transform your data using a different method called in-warehouse data transformation. Rather than waiting for the transformation before loading data into the warehouse, you can load your data directly into the warehouse and transform it there. 

For traditional multistage data transformation, here’s a step-by-step guide to the process: 

Step 1: Gather data and define goals

Identify all the sources from which you’ll need to gather data. Know what kinds of file types these sources will give you. You’ll also need to have a goal in mind so you know what format the data will need to be in. That will help you transform the data correctly. This step is also a good time to do a quick quality check on your data. If you’re importing poor-quality data, it could cause issues with the transformation process and the insights down the road. 

Step 2: Data mapping

This step involves mapping the data fields from your source data and matching the corresponding fields in the target format. This will make sure your data transforms correctly and gives you consistent results. 

Step 3: Transform your data

Create a script (or use a pre-made tool) to transform your data in the format you want to store it. The script or tool will help clean and reformat your data. 

Step 4: Data Validate

After transforming your data, you should check it and ensure the transformation is accurate and complete. This is also a good time to write down the rules and process you used so that if anyone after you wants to repeat the process, they know exactly how you got your results. 

Data transformation methods and techniques 

Once your data has been extracted, it’s time to transform it. There are various techniques to transform data, and it’s important to know which methods will yield the type of data insights you’re looking for. Depending on your end goal, you can consider some of these methods. 

Data smoothing

Imagine a graph with thousands of data points scattered across the quadrants. Data smoothing is like drawing a trend line through the densest clumps of data. When you use data smoothing techniques, you cut out the outliers, noise, and unhelpful data points. This allows you to see trends and uncover patterns you may not have noticed. 

Data aggregation

When using data aggregation techniques, you combine two or more separate data sets for extra insights. The different data sets have meaning on their own, but when they’re compared to each other, they offer synergistic insights. For example, you can aggregate advertising spend from a certain period with site sales from the same period. This can give you an idea of your advertising campaigns’ effectiveness, capturing revenue that may not have been directly funneled through link clicks or attributed to cookies. 

Data derivation

Derivation is taking existing data and deriving a new insight from it. If you need to build a new warehouse to ship more products, you can look at a map of where your target demographic resides and cross-reference it with cheap property prices to derive a data set of feasible places to build a new facility. 

Types of ETL transformation and examples

You can tailor results and gain more business insights when you use different data transformations. Here are some of the data transformations in ETL you can use to clean and organize the data in a way that’s helpful to you:

• Data splitting. Splitting is when you split apart data. For example, if you have people’s names as part of your data, you can split it into first names and last names. 
• Data integration. If you’re combining data from multiple sources, you may have the same kind of data under different names. Perhaps one data source from customers has “income” and data from another source has “net income.” Are these the same? If so, they’ll need to be integrated; if not, you may need to rename one data category for clarification. 
• Data joining. Joining is when you need to add certain data points together. For example, if you’re trying to calculate your expenses, you’ll need to join together employee salaries, advertising spend, costs of software platforms, etc. 
• Deduplication. ETL data transformation finds duplicate data points and removes the extras so you have a more accurate data set.
• Format revision. When gathering data from multiple sources, the data may be in different formats. For example, you may have dates in your data set, some of which are month-day-year and some day-month-year. The format revision phase makes sure all the data is in the same format, making it consistent and preparing it for easy storage and retrieval from a database later. 

ETL transformation best practices

Data transformation in ETL is a complicated process. If not done carefully, the transformation stage of ETL can lead to inaccurate data being uploaded into your data warehouse. Poorly transformed data offers misguided insights, incorrect generalizations, and may even cause errors or crashes in other software systems. To ensure a successful data transformation, be sure to use best practices. 

One best practice is to design efficient and scalable ETL workflows. When ETL workflows are streamlined, they can handle larger volumes of data and process batches faster. You can make your workflows more efficient by employing best practices such as standardizing naming conventions before you start transformation, profiling your data carefully, and adding metadata to enrich log data. 

Another best practice is to implement data validation and error handling. At least a few errors are inevitable during a first-time transformation, so it’s best to design a process to handle malformed or incomplete data before it causes problems. You’ll also want to create a thorough quality check system to validate your data. Check to make sure that all necessary fields are included, any excluded fields have been removed, and that your data is being stored as the correct data type and in the correct format. 

It’s also important to optimize performance and resource utilization. ETL transformations can be time-consuming and take a lot of code generation. You can consider parallel processing, which involves distributing data processing tasks across multiple nodes. Parallel processing can help you save time and maximize resource utilization. If you’re working with large amounts of data, incremental loading and data compression can help you save storage space, reduce loading time, and help the code execute faster. If you’re still struggling with a lack of processing power, you can use a scheduler to optimize ETL workloads. Schedulers distribute tasks across multiple servers, which takes less time and fewer resources. 

No SQL skills? No problem. At Domo, we believe data transformation is for everyone. You shouldn’t have to be a professional data analyst to reformat data and gain insights. Our ETL software easily combines data from multiple sources with DataFusions—no SQL required! If you do happen to be a data analyst nerd, though, Domo’s rich platform allows you to complete processing on a dataset in R or Python directly, as part of ETL data processing.