ETL vs ELT: Data Integration Explained
Key Points
- Data integration moves and prepares data across sources and targets for reporting, analytics, AI, and other use cases, acting like a business’s water filtration system.
- ETL (extract‑transform‑load) cleanses data in a central processing stage before loading it into a target, making it ideal for large, complex, or sensitive datasets and for pre‑filtering data before it reaches the cloud.
- Common ETL use cases include migrating data to cloud warehouses, processing data from cloud applications, and handling financial or marketing data where PII must be removed upstream.
- ELT (extract‑load‑transform) loads raw data directly into a modern cloud warehouse or lakehouse and leverages scalable compute there to transform it, which suits analytics workloads and SQL/DBT‑based pipelines.
- While ELT offers speed and flexibility for scalable cloud processing, unpredictable data spikes can drive up costs, so the choice between ETL and ELT depends on architecture, data volume, and cost considerations.
Sections
- Untitled Section
- Choosing Between ETL and ELT - The passage compares ETL and ELT data pipelines, outlining how ETL pre‑processes for cost, compliance, and heavy transformations, while ELT leverages cloud warehouse compute for fast, scalable analytics on raw data.
- Choosing Between ETL, ELT, TETL - The speaker outlines that ETL, ELT, and TETL are interchangeable data‑pipeline tools whose selection hinges on infrastructure, use case, and compliance, but all aim to deliver clean, trusted data to the right users at the right time.
Full Transcript
# ETL vs ELT: Data Integration Explained **Source:** [https://www.youtube.com/watch?v=KIv2Na2-u24](https://www.youtube.com/watch?v=KIv2Na2-u24) **Duration:** 00:06:38 ## Summary - Data integration moves and prepares data across sources and targets for reporting, analytics, AI, and other use cases, acting like a business’s water filtration system. - ETL (extract‑transform‑load) cleanses data in a central processing stage before loading it into a target, making it ideal for large, complex, or sensitive datasets and for pre‑filtering data before it reaches the cloud. - Common ETL use cases include migrating data to cloud warehouses, processing data from cloud applications, and handling financial or marketing data where PII must be removed upstream. - ELT (extract‑load‑transform) loads raw data directly into a modern cloud warehouse or lakehouse and leverages scalable compute there to transform it, which suits analytics workloads and SQL/DBT‑based pipelines. - While ELT offers speed and flexibility for scalable cloud processing, unpredictable data spikes can drive up costs, so the choice between ETL and ELT depends on architecture, data volume, and cost considerations. ## Sections - [00:00:00](https://www.youtube.com/watch?v=KIv2Na2-u24&t=0s) **Untitled Section** - - [00:03:04](https://www.youtube.com/watch?v=KIv2Na2-u24&t=184s) **Choosing Between ETL and ELT** - The passage compares ETL and ELT data pipelines, outlining how ETL pre‑processes for cost, compliance, and heavy transformations, while ELT leverages cloud warehouse compute for fast, scalable analytics on raw data. - [00:06:10](https://www.youtube.com/watch?v=KIv2Na2-u24&t=370s) **Choosing Between ETL, ELT, TETL** - The speaker outlines that ETL, ELT, and TETL are interchangeable data‑pipeline tools whose selection hinges on infrastructure, use case, and compliance, but all aim to deliver clean, trusted data to the right users at the right time. ## Full Transcript
Data integration is the process of moving and preparing data between different sources and targets.
So, it can be used for reporting, analytics, or AI or a variety of other use cases.
If we think about data integration as the water filtration system for your business, then ETL and ELT are two different ways to treat your water before it reaches the tap.
Both aim to deliver clean usable data, but they differ in where the cleaning happens.
Let's start with ETL.
This stands for extract,
transform,
and load.
This is the traditional model where you transform the water in flight.
You extract dirty water from the source and pipe it to a central treatment facility where you clean the water thoroughly,
and then you pipe the water into your city reservoir where it can be used by residents.
So, in data terms, we extract raw data from operational source systems like a database management system or a data warehouse, a data lake, even an application.
The list goes on and on.
We then transform it through cleansing, joining, enrichment using a data processing engine,
such as third-party or open source.
Then finally, we load the clean, structured output into a target system like a cloud data warehouse or a data lakehouse.
ETL is fantastic for handling large volumes of complex data.
It's also great for sensitive data because we're able to remove and transform any type of PII before that data hits the downstream system.
One of the most common use cases is moving data to cloud.
ETL filters and prepares data before it hits the cloud.
By cleaning and optimizing the data upstream, you avoid expensive Cloud compute, just like keeping grit out of your pipes, so you don't drive up filtration costs at home.
Other use cases best fit for ETL include, processing data from cloud applications, Also, when we're working with financial systems and even marketing tools.
Now let's look at ELT.
We extract,
load, and then transform.
This flips the traditional model of ETL.
Here, you pipe the raw water directly into the reservoir.
And then treat it inside the reservoir using built-in filtration systems.
In data terms, we extract raw data from the source, load directly into a warehouse or a lake house,
and then transform using the compute power of that cloud system, like SQL engines or push-down processing.
ELT is great when you're dealing with modern cloud data warehouses,
CDWs, and you want to take advantage of their scalable compute.
You can then push data integration tasks to those data warehouses through query engines available to lake houses.
However, if your data volume spike or become unpredictable, costs can quickly balloon.
A top use case for ELT is for analytics workloads.
You load raw data into a cloud data platform and then transform it there using scalable cloud compute, such as when implementing a medallion architecture.
It's fast and perfect for teams using SQL or DBT to turn data into insights.
From a technical perspective, the decision isn't just about architecture, it's also about performance, cost, and compliance.
ETL lets you optimize compute for heavy transformations before data hits your cloud platform.
Often providing cost savings.
It's also great for when compliance is a concern, because sensitive data including PII can be filtered upfront.
And finally, ETL often takes advantage of powerful engines, making it well-suited for large volumes and complex transformations.
It's very performant.
ELT, on the other hand, prioritizes data loading by moving raw data first and transforming it later using the power of cloud platforms,
but because raw data lands before it's cleaned up, you need a strong governance and quality framework,
and additionally, an eye on optimizing workloads once the data is loaded.
You've heard of ETL and ELT, but there's another flavor of where the transformation happens and that is TETL.
Stands for transform, extract, transform, and load.
TETL introduces a pre-transformation step at the source before extraction.
Think of this like a lightweight filter for water before it goes through your pipes.
You clean the water early so it doesn't clog up your system.
Then after it's moved, you do the heavier transformation before loading it into your target system.
ETL and ELT and TETL are just tools in your data plumbing kit.
The right choice depends on your infrastructure, your use case, and your compliance needs.
So whether you clean the data before or after loading or both, your goal remains the same.
Deliver clean trusted data to the right people at the right time.