ELT vs ETL: Everything you need to know

ELT vs ETL: which is better?

Well, if you have a lot of data and you need to move it, you have basically 2 options. First, if it’s a one-time thing and a truly massive amount of data, you might try sneakernet … as in, move it on hard drives. (Believe it or not, both Amazon and Google have options for this.) For most people, however, you’re going to do some variety of ELT or ELT, especially if the data transfer will be an ongoing thing.

Which raises the obvious question …

ELT vs ETL … what’s the difference? And where should you use each one? 

That’s exactly what we’re going to answer right now.

ELT vs ETL in 5 bullet points

There’s a lot to be said about ELT vs ETL. For starters, ELT is Extract, Load, Transform, and ETL is Extract, Transform, and Load.

But you knew that already. 

Here’s the too-long-didn’t read with just a bit more insight:

• Order matters
  ELT loads data into the target system before transforming it, while ETL transforms data before loading
• ELT is faster and more scalable
  ELT is generally faster and more scalable for large datasets, thanks to the processing power of modern data warehouses
• Cloud computing is driving change
  Cloud computing has been a driving force behind the shift from ETL to ELT, enabling greater scalability and cost-efficiency
• ELT wins in some areas
  ELT is often preferred for big data, AI, and real-time analytics use cases
• ETL remains relevant
  ETL remains relevant for legacy systems, highly structured data, and specific compliance needs

When sneakernet isn’t an option, businesses use data integration methods like ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform). These methods help get and use large amounts of data quickly and safely. 

In both ETL and ELT, the process starts by taking raw data from different sources. Then, we either  transform this data into a format that is easy to use, or load it into a target system. Finally, the data gets loaded into a data warehouse or transformed, depending on which method is used.

With the growth of big data and cloud technology, many businesses are moving from the old ETL process to the newer ELT approach, but each method has its own pros and cons.

The basics: understanding ELT vs ETL

Both ELT and ETL are important for changing raw data into useful insights, but they are different in how they work. 

ETL uses an older method, changing data before sending it to a target system. This way, only clean and organized data goes into the data warehouse … at least theoretically. (In the real world, stuff sometimes happens.) ELT, on the other hand, uses the benefits of modern data integration and cloud data services which have their own extensive transformation capabilities. ELT loads the raw data into the target system or systems first. Any changes happen later, right inside the data warehouse or data lake, usually at the direct instigation of a BI team or data analyst.

Let’s look at ETL first.

ETL is perhaps the most common legacy method of moving and processing data. It focuses on getting data, then cleaning and organizing it, and then saving it into a data warehouse: extract, transform, and load. 

The transformation step in the ETL process is very important. It changes raw data into a format that works with the target data storage system. This step can include data cleansing, filtering, aggregation, and checking the data based on set rules and needs. It could also include enriching, and it likely includes converting the data structure — including rows, columns, and formats — to fit a very structured target system.

ETL works well because it has a clear process. It makes sure that only processed and validated data is added to traditional data warehouses. It’s perfect: clean, efficient (especially in terms of human handling and babysitting), and effective.

Still, there are some issues with this method, especially with the large amounts and types of data we see today in big data.

So what’s the alternative?

ELT.

ELT is an important part of modern data integration. It focuses on managing large amounts of data effectively. In ELT, just like in ETL, the first step is data extraction. This step collects raw data from different sources, such as databases, applications, and sensors. The second step is where things change: raw data is loaded directly into a cloud data warehouse like Amazon Redshift or a data lake, without changing it right away.

The transformation of data happens inside the data warehouse. This method uses the impressive processing power and scalability of cloud platforms, which allow for complex changes to large datasets. ELT works very well with unstructured or semi-structured data, and provides the flexibility to transform data as needed for business intelligence or analytics purposes.

That last point is important.

ELT gives you raw data. If you decide some months from now that you want slightly different insights from your data, you can go back and use all the data you collected in its raw form to extract those insights. With ETL, the challenge is that the cleansing and formatting step might have thrown away data that — at the time — seemed like unnecessary or even garbage data.

A shift in data strategy …

The rise of ELT is more than just a new method; it shows a major change in how we handle data. This change is driven by new technology and the need for quick data analytics. To fully grasp this shift, we have to look at the past of data management and what led to the growth of ELT.

In the early days of data warehousing, storing data was costly, and processing power was low. ETL worked well, because carefully cleaning and organizing data sets before putting them into costly, local data warehouses saved money on storage.

Transformation aimed to lower the amount and complexity of data. It helped use storage space more efficiently. This traditional method keeps data integrity and consistency but it can also make the data integration process more complicated and time-consuming.

When we look at ELT vs ETL, we see why.

The rise of cloud computing changed how we manage data. It made data storage solutions more scalable and affordable. This boost in processing power, along with the fast growth of large data sets, makes ELT a better option in many cases.

With ELT, businesses can quickly load raw data into a data lake or cloud data warehouse, where they can now get very cheap storage. Then, they can wait to transform this data until it is needed. This offers more flexibility as business intelligence needs change. So ELT can lower initial costs while making data transformation easier. 

This approach is great for organizations that want to improve their data analytics capabilities.

When to use each: ELT vs ETL

The ELT vs ETL conversation remains an active conversation because there are still use cases where ETL makes sense, particularly with legacy systems.

Sure, ELT is quickly becoming the best choice for many current data integration needs. But both ETL and ELT have their own pros and cons, and choosing the right method for you and your data depends on different factors, including data volume, data types, business needs, and the systems you already have in place.

Choose ELT for cases like these:

• Big data, unstructured data
  ELT is great for working with huge amounts of data. This is especially true when it is unstructured or semi-structured. It’s generally better to put raw data into your data lake first and then change it later. This is often more efficient than trying to organize everything before loading.
• Data exploration
  ELT offers more options for exploring data. You get more flexibility and agility over time with ELT. You do not need to set all the transformation rules before loading the data. This lets analysts try out different business rules and data models without needing to reload all the data each time.
• AI, ML applications
  ELT works really well for AI use cases where you often need to process large and varied datasets, including unstructured data such as text and images.

Consider ETL for cases like these:

• Legacy systems
  ETL is still a good choice when dealing with old systems that follow strict data rules and need a high level of data integrity.
• Compliance, sensitive data
  When rules, regulations, or laws say you must change or hide certain data before storing it, ETL’s organized way can help you guarantee that you meet those rules. Essentially, you become compliant by design, and don’t risk accidentally storing data that could get you in trouble later on.
• Optimized data warehouses
  For businesses that use traditional local data warehouses with limited storage, ETL’s pre-loading transformations can be very useful.

Summing it all up

In conclusion, ELT and ETL are both very important for data integration strategies. 

ELT is becoming a better option for many because it can be more scalable and efficient, especially with cloud computing and huge datasets. 

But knowing the different technical aspects of ELT and ETL — and how they intersect with your needs and your technology platforms — is key for making smart choices about data processing workflows. ETL is useful in some cases, but ELT can make you more adaptable and flexible for the future. 

Organizations should check their needs and data processing requirements to find the best method for them, and consider switching to ELT to improve data security and scalability, ultimately gaining a competitive advantage in today’s data-focused world.

Data helps you win. 

But only if you are smart about how you collect, store, and use that data.