Yes, ETL and ELT are similar. After all, they’re the same three steps: extraction, transforming and loading, in a different order.
However, and crucially, it's better to use them in different circumstances. When you deploy them at the right times, you’ll enjoy important benefits. For example, easier regulatory compliance or improved agility.
Considering this, let’s explore the differences between ETL and ELT. And, let’s look at the benefits of each approach respectively. But first, let's define our terms.
Here’s a straightforward definition of ETL:
Extract, transform, load (ETL) is the procedure of moving data from one or more sources to a new location. Data gets cleaned and transformed into a new format/structure in between extraction and loading into the destination.
Typically, ETL merges data for storage in data lakes, data hubs, and data warehouses.
If you’d like to explore ETL in more detail, head over to our dedicated resource page.
Here’s a to-the-point definition of ELT:
Extract, load, transform (ELT) is when you extract data from source destination(s) then move it to a target system. It’s in this new location that the data transformation then occurs.
Thanks to cloud-based data warehousing solutions, such as Amazon Redshift, Snowflake and BigQuery, companies can use scalable cloud computing to perform transformations in the cloud.
As you can imagine, there are different benefits when you choose ETL or ELT. Sometimes, you’ll want to transform data before you load it into a destination; other times, it’s better to load it first.
There are many times when ETL is the best route to go. As a rule, it brings more control over your data.
Let’s look at some of the specific advantages.
When you perform transformations before data goes to the cloud, you’ll find it easier to maintain compliance with stringent regulations such as GDPR and HIPAA.
Whether it’s performing data anonymization or encrypting parts of your data, it’s more secure and straightforward to perform this before you load it into the cloud. Indeed, just by moving data to the cloud, you might breach certain regulations. For example, if you move European data to non-European data centers, you might breach GDPR.
Using ETL also means that you don't have to store all the data. This can be extremely important for complying with regulations such as HIPAA. After all, there's a big difference between keeping data in memory vs. actually storing the data. With ELT you need to store all the data, whereas, with ETL, it's transformed before it's warehoused.
ETL is also the best option when you have other worries around sensitive data.
If your business needs data cleaning (perhaps if it contains sensitive client information), it’s safer to transform it before it goes to the cloud. This makes ETL more attractive for sectors such as financial services or the legal sector where there’s so much sensitive data.
The likelihood of data issues is also reduced by the fact that, as previously stated, you don't need to actually store all your sensitive data with ETL. It's transformed before it's loaded.
Another reason ETL is better for your sensitive data is that there's an overall reduced risk of hackers gaining access to your data.
Did you know that up to a third of cloud spend is not tracked and ends up wasted?
When companies mindlessly perform a ‘lift and shift’ into the cloud, they can end up paying excessively for the storage of unsorted and unmanageable volumes of data.
If you’d rather save money and ensure you run a tighter ship financially, transforming data before it goes to data warehouses is a good move.
Having looked at occasions when ETL is the right path to take, let’s now explore some of the positives of transforming your data in the cloud instead.
When you use ELT over ETL, there’s more potential for agile collaboration on data projects. That’s because, with so much raw data in the cloud, everything is open for data analysts and engineers to work on.
Now, your teams can work flexibly on innovative projects to unlock the kind of value you’d struggle to see if you’d transformed all your data beforehand. This can look very appealing to large firms with distributed workforces who are eager for innovation.
Another reason you might choose ELT is if you need to take advantage of the scalability of cloud computing. With the quasi-infinite resources of the cloud, nothing is stopping you taking on transformations of any scale.
In situations like these, it’s better to use cloud infrastructure to turn on resources instantly and get the power you need.
As we’ve seen, there are advantages to both ETL and ELT.
When you use ETL, it’s easier to look after your more sensitive data and control data pipelines if compliance is a concern. On the other hand, ELT is better when you’re eager to harness the scalability of cloud resources for big transformations, or when you want to encourage agile collaboration on data.
However, whether you choose ETL or ELT, it’s important to deploy data tools that enable you to solve for all your data challenges. Automation also has to be at the heart of your data pipelines. Otherwise, you’ll eat up the valuable time of your IT teams performing tedious manual tasks, whether it’s manually cleaning data or loading it into the cloud.
If you’d like to chat with one of our expert team and explore whether you need ETL or ELT, or to find out if CloverDX could help solve your data needs, please reach out for a chat today.