If you have ever dipped your toes into data engineering (or found yourself knee-deep in an Alteryx workflow at 1 a.m.), you have probably come across the eternal debate between ETL and ELT.
When I first started learning about data pipelines, these two acronyms kept appearing like rival teams in a championship match. They looked similar, sounded similar, but played very differently. Understanding why they differ completely changed how I approach data transformation today.
So, what exactly are ETL and ELT?
At first glance, they both do the same thing: move data from one place to another so it can be analysed.
The key difference lies in when the transformation happens.
- ETL (Extract, Transform, Load) means data is extracted from the source, transformed outside the destination, and then loaded into the warehouse.
- ELT (Extract, Load, Transform) means data is extracted, loaded straight into the warehouse, and then transformed inside it.
It may seem like a small change in order, but it reshapes how teams handle data, particularly in the era of cloud computing.
The classic ETL: Old-school reliability
ETL has been around for decades and is often considered the original data pipeline method. It works well when handling structured data and on-premise environments.
The process looks like this:
- Extract – Data is pulled from multiple sources such as APIs, databases, or flat files.
- Transform – The data is cleaned, shaped, and formatted using an external engine or ETL tool.
- Load – The transformed data is loaded into a data warehouse, ready for analysis.
When I first started exploring ETL, it reminded me of preparing a game build for release: a lot of polishing, testing, and packaging before it finally goes live. It is neat, structured, and predictable.
However, there is one major drawback: scalability. As data volumes increase, pre-load transformations can become bottlenecks. It is a bit like trying to compress a 10-gigabyte texture pack before uploading it over a slow connection. It will work, but it will take time.
ELT: The cloud-native powerhouse
Then came ELT, the modern approach that flips the traditional process.
With cloud data warehouses such as Snowflake, BigQuery, and Redshift, you no longer need to transform data before loading it. The cloud does the heavy lifting.
The ELT process is straightforward:
- Extract – Collect raw data from your sources.
- Load – Push it directly into the data warehouse in its raw form.
- Transform – Shape the data inside the warehouse using SQL or other in-built tools.
I prefer to load everything first, view the raw data and then experiment with transformations inside the warehouse. It allows you to iterate quickly without constantly rebuilding the pipeline.
Why ELT is winning in modern data stacks
ELT is not just a trend; it is a practical shift in how modern data teams work.
Here is why it has become so widely adopted:
Leverages cloud computing power
Rather than relying on local servers or external transformation tools, ELT uses the processing capabilities of cloud warehouses. This makes transformations faster and easier to scale.
Faster data availability
Data lands in the warehouse almost instantly, meaning analysts can start exploring without waiting for all transformations to complete.
Cost efficiency
By removing the need for external ETL engines, teams save on infrastructure and maintenance costs. The warehouse manages the workload efficiently and at scale.
Flexibility and iteration
ELT supports rapid experimentation. You can transform, test, and refine data directly within the warehouse, which is ideal for agile teams that need to adapt quickly.
Data accessibility
Because the raw data is stored centrally, more people can access and explore it. This supports a culture of data transparency and collaboration across teams.
ETL vs ELT: Which one is better?
The honest answer is that it depends on the context.
If you are working in a legacy environment with strict data governance or heavily structured systems, ETL might still make sense. But for modern, cloud-based data environments that need scalability, flexibility, and speed, ELT is the future.
Final thoughts
Whether you are building a game analytics pipeline or designing a client-facing dashboard, understanding the difference between ETL and ELT is essential.
It is not just about the order of operations but about speed, scalability, and creative freedom in how you work with data.
The best approach is the one that keeps your data accurate, your processes efficient, and your curiosity alive.