Although ETL (extract, transform, load) transformations all share some common traits, they come in many different forms. Depending on which types of data you are working with and what you want to do with it, you may choose to leverage a wide variety of different ELT data transformations.

This article breaks down 9 of the most common ETL transformation types, highlighting what each transformation does and common use cases for it. It also provides guidance on how to get the highest performance from ETL transformations, regardless of which type of transformation you’re working with.

What is ETL data transformation?

Let’s start by defining what ETL data transformation means: ETL data transformation is the process of collecting data, then modifying it in some way before delivering it to its destination. Put another way, ETL data transformation is a type of operation that transforms data during the data integration process.

ETL is important because it makes it possible to turn raw data into a schema or format that can be easily read by a data visualization tool. Thus, if you have “messy” data that you struggle to analyze in its current form, ETL provides a way of transforming the data into a format you can work with more effectively.

The data integration process

To place the meaning of ETL data transformation in context, let’s step back and define the entire data integration process, since ETL data transformation is one step in that process.

Data integration refers to the set of operations that businesses use to extract unstructured data from one or more sources, transform it in some way (like aggregating or deduplicating it, for example) and then load it into a destination (such as a data lake or warehouse) – hence the acronym ETL, which summarizes the three main steps that take place during data integration. The ETL process also often involves structuring data into a standardized format, like JSON or XML.

When people talk about ETL data transformation, they’re referring to the second step in that process – transformation. After extracting data from one or more sources, you can transform it in various ways prior to delivering it to its ultimate destination.

The importance of ETL data transformation

The main reason why ETL data transformation is important is pretty straightforward: Data in its “raw” form – meaning the form in which it exists inside its original source, such as an application or database – is often not optimized for the use cases it needs to support. It may contain missing or redundant information, for example, or it might include a lot of data that isn’t actually necessary and should be filtered out.

Transformation makes it possible to improve the data during the ETL process. That way, the data is already optimized for its intended use case by the time it arrives at its destination.

If you wanted, you could wait until data has been delivered to its destination to perform transformations – in which case you’d be performing ELT (extract, load, transform) instead of ETL, because the transformation step would come last rather than in the middle. Sometimes, ELT makes sense, especially if the destination where your data will be stored following extraction provides better capabilities for transforming the data than the tools you use to extract the data. This might be the case if, for example, you’re working with a specialized type of data that your data integration tools are not designed to support, but that you move into an analytics platform that is designed for that particular data type.

In general, however, performing data transformation right after the extraction process is a more efficient and effective approach because it ensures that data arrives at its destination primed for the operations that you will perform on it. You don’t have to waste time or compute resources processing the data at the destination. In addition, ETL data transformation operations like deduplication and filtering can reduce the amount of data that you need to send to a destination, which may in turn lead to lower costs in situations where your destinations charge based on the total volume of data they ingest or store.

9 Types of ETL Transformation

As we mentioned, there are many types of data transformations that you can perform during the ETL process. Here’s a look at the 9 most common ones.

#1. Data binning or bucketing

It often happens that you have a data set that contains many values that are distinct from each other, but are closely related. If the differences between the values are not significant enough for it to be necessary to retain each individual value, you can simplify data analytics or statistical processes that you want to perform on the data by consolidating similar sets of values into bins or buckets – a process known as data binning or bucketing.

Example

For example, imagine that your company tracks the ages of customers, as well as their purchasing history. If you wanted to analyze how customer age correlates with the frequency or volume of purchases, it would probably make sense to perform the analysis based on a set of age ranges, rather than tracking the individual age of each customer. For instance, you could break your customers into age ranges of 21-30 years, 31-40, 41-50, 51-60 and older than 60. Each age range would function as a bin or bucket.

In this case, you’d reduce the total individual values you need to contend with when performing your analysis, which would make it easier and faster to analyze the data. You’d lose a small amount of information through the binning or bucketing process, but it would probably not affect the reliability of your outcomes.

Use cases

Data binning or bucketing are a popular ETL transformation type in the context of data science and statistical operations because they simplify the analytics that teams want to run on their data without meaningfully degrading the value of the analysis. Binning and bucketing can also sometimes prove useful for machine learning workloads, where they can speed data processing by reducing the number of individual values that algorithms have to interpret.

#2. Data aggregation

Data aggregation is the process of combining data from multiple sources into a single data set, where the original information is summarized in ways that can speed and simplify analysis. You can perform data aggregation using source data of different types, provided the data shares enough similarities to make it possible to summarize key characteristics in some way.

Data aggregation is important because the typical business works with many different data sources and types, and analyzing each one individually is often not desirable. By merging multiple data sets into a single, consolidated set, data aggregation can significantly simplify data analytics workflows.

Example

For example, imagine that your company has multiple customer-facing websites, and you want to analyze data about how customers interact with the websites so that you can draw high-level conclusions about your customers’ digital behavior. One way to do this would be to analyze each website’s data individually. But that would be inefficient (because you’d have to run multiple analytics operations, one for each site) and unnecessary if your goal is to understand customer behavior across all websites, not individual sites. A better approach would be to aggregate the data from all of the websites, and then perform a single analysis.

Use cases

It’s difficult to associate data aggregation with a particular use case because it can be valuable across such a wide variety of scenarios. From analyzing IT data, to working with customer data, to data science operations and beyond, data aggregation is useful in any situation where you have multiple data sources and you want to analyze them collectively.

#3. Data integration

Data integration is the process of merging multiple data sources into a common data set, but without summarizing them. This means that data integration is similar to data aggregation, with the main difference being that data is not summarized when it is integrated.

Example

As an example, imagine that you have multiple databases, each containing different types of information. You can’t easily summarize the data because it’s not consistent enough. However, you might still want to be able to combine it so that you could run a single set of queries against all of the data, rather than having to query each database independently. You could do this by integrating all of the databases into a single data set during the ETL process.

Use cases

Like data aggregation, data integration is valuable across many use cases. It’s especially useful in scenarios where you have not just multiple data sources but also multiple data types, which makes it harder to summarize the data, but you still want to work with the data in a centralized way.

#4. Data cleansing

Data cleansing is the process of removing incomplete, inaccurate or otherwise problematic information from a data set during a data integration process. It’s a means of improving data quality, which makes data analytics results more accurate.

Example

For instance, consider a data set that includes a set of personal names. To cleanse the data, you could run a transformation process that looks for examples of names that are likely to be misspelled – such as Mihcael or Tohmas. In most cases, those names were probably entered incorrectly due to typos, and cleansing the data will help to eliminate manual data entries  like inaccurate names in analytics results.

Use cases

Data cleansing can be useful in a variety of contexts, although it’s especially valuable when dealing with data that is manually entered by humans. Such data is more prone to include errors and inconsistencies than machine-generated data.

#5. Data deduplication

Data deduplication is the removal of redundant information from a data set. It’s useful because it reduces the overall size of data sets. It can also improve data quality in cases where redundant information exists in a data set by mistake.

Example

As a simple example, take a database that contains two columns that record customer names. There’s likely no reason why you need these redundant columns, and so you can delete one column to deduplicate your data.

Use cases

Data deduplication is valuable when dealing with both human-generated and machine-generated data, since both types of data can contain redundant information. It’s especially useful in situations where you want to minimize the total volume of data that you have to store or ingest into your destination.

#6. Data derivation

Data derivation is the process of generating new data based on existing data. Data derivation is useful in situations where you can reliably create new data based on data you already have.

Example

For example, imagine that you are a retailer and you have a database that records customer addresses as well as their purchasing history. If you wanted to know how the proximity of a customer’s home to your store correlates with purchasing trends, you could derive the distance based on the address data.

In this case, you would create a new set of data values – the distance from the store – based on data you collected originally. You could then perform analytics operations using these new values.

Use cases

Among the most common use cases for data derivation is working with data related to customers, since data derivation makes it possible to run analytics based on new types of customer characteristics that you might not have included in an original data set.

#7. Data filtering

Data filtering means filtering out certain parts of a data set during the ETL process. You can then work with only the filtered data, rather than the complete original data set. In this way, filtering can reduce the total amount of data you have to work with, while also reducing the complexity of your data set.

Example

As an example of data filtering, imagine that you have a set of log files from a website and you want to analyze how many people connect to the website based on time of day. In this case, the only log data that you really need is timestamps that show when connections occurred. You can filter out other data, such as which content from the site was associated with each request or how long the request took to fulfill.

Use cases

Data filtering is useful in a wide range of contexts. Any time you have a data set that contains more information than you need for a particular use case, data filtering will help you work with the data more efficiently.

#8. Data joining

Data joining means combining multiple data sets that contain the same types of data. Data joining can reduce the complexity of data sets, as well as eliminate redundant categories of data.

Example

A classic example of data joining is when you have two columns within a database that contain the same type of information. For instance, you might have one column that contains customers’ first and last names, and a second column that lists only their last names. You could join the columns together into a single column that records first and last names.

Use cases

You’re most likely to encounter data joining in use cases that deal with data stored in relational databases. Data joins are more difficult to perform in most other contexts because they only work well when you have two sets of information that are very similar in type and structure – which is rare outside of relational databases because most other ways of storing data lack the consistent structure of databases.

#9. Data splitting

Data splitting is the division of data into two or more subsets. Typically, the purpose of data splitting is to allocate different subsets of data for different purposes.

Example

For example, imagine that you’re training a machine learning model. The data set you’ll train the model on can be split into three parts:

• Training data, which you’ll use for the actual training.
• Validation data, which you’ll use to validate training during the training process.
• Testing data, which you’ll use to test your model after it’s trained.

By splitting your data in this way, you ensure that you devote different data to each step in the training process and avoid issues like performing tests based on the same data you used for training (which could lead to inaccurate test results because the test data would not be independent from the training data).

Use cases

Data splitting is most commonly used in workflows related to machine learning, especially model training. However, splitting can be useful in any scenario where you need to retain independent sets of data in order to validate or test analytics results.

Putting data to better use with ETL transformations

Simply having data is not enough to create value. In many cases, you also need a way to process and structure data so that you can work with it effectively.

ETL data transformations ensure that businesses can transform their data into whichever form they need to achieve these goals. By identifying the data transformations that make the most sense based on the types of data you are dealing with and your goals for using the data, you can maximize the value you get from your data.