Does ELT vs. ETL Even Still Matter?
Data teams continue to debate which is better: ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform). Meanwhile, they are overspending, pipelines are breaking, or no one is utilizing the data. So, does this debate even matter anymore?
Ok perhaps that’s harsh but I do always find it a little funny when people still talk about ELT vs ETL.
After all, teams that do ELT will often really do ELTtLtTTL.
And data teams that do ETL, still likely have further steps as well.
Nevertheless, I still hear people debate this topic….ETL, no ELT!
Instead of a rigid ETL vs. ELT debate, data leaders must shift their perspective and ask new questions.
ETL vs. ELT: Overview
ETL is the traditional data integration process. Raw data is extracted from sources and transformed before loading into a target data warehouse. The advantages of this process are that it helps ensure data quality and compliance before storage. In terms of when it’s best used, there is likely a lot of debate. After all, this article is all about ETL vs ELT. Many data engineers just always use ETL.
It’s particularly helpful when you’re working with structured data, have strict compliance requirements (think HIPAA, GDPR, financial regulations) and in turn need to limit how much data gets pushed around. It also can help reduce costs in terms of compute because you’re not spending Snowflake or Datatbricks credits. Instead, you’re usually using whatever compute the ETL system is hosted in.
ELT is viewed as the modern version. Truthfully, it’s really just the same steps in the same order. It loads the raw data directly into a cloud data warehouse, such as Snowflake, BigQuery, or Redshift. Transformation occurs after it is loaded inside the data warehouse. The advantages of this approach at least in my experience is that you get the data into your data warehouse faster. In turn, it’s easier to figure out what the data itself looks like. I do always like to point out to most teams, that there is no getting around the “T”. You’ve always got the transformation step.
I’d say that people on both sides of the ETL versus ELT argument usually feel pretty strongly about their approach. And sure there are pros and cons to both, but I do have to say, many teams don’t often design their systems strictly enough to really get said advantages.
Why This Debate Originally Mattered
The debate between ETL and ELT was primarily driven…well if I am being honest…by marketing. But if you were to look it up I am sure you’d see something about hardware limitations and budget constraints instead of something like technical preference.
If you were to put it into categories it’d probably break down into things like.
- Compute constraints: For years, warehousing systems couldn’t handle heavy, simultaneous computation and storage. These constraints meant everything had to go through the data team, and transforms took a long time to build.
- Storage limitations: Storage was expensive, forcing companies to transform and narrow data to only what was needed. They would then load it into the warehouse.
- Warehouse capabilities: Legacy databases lacked the parallel processing power of modern cloud warehouses, such as Amazon Redshift or Snowflake.
- Skill set divisions: ETL was the domain of data engineers writing pipeline code, while ELT pushed transformation work toward analysts using SQL in the warehouse. The debate was partly a turf war over who owned the transformation layer.
- Tooling ecosystems: ETL tools like Informatica, Talend, and SSIS dominated for decades and shaped how teams thought about pipelines. Choosing ETL vs ELT often meant committing to entirely different vendors, skill sets, and licensing costs.
If you have some other thoughts about the debate, I’d always love to hear them!
What Changed?
The debate between ETL vs. ELT is fading. Or at least for some. It’s likely just drowned out by all the LLM discussions.
But also because many companies have likely adopted some weird hybrid.
Modern warehouses, like Snowflake, BigQuery, and Databricks, have shifted the constraints of data engineering. Cheap storage and scalability are making ELT an easy choice for many.
I say easy. But it’s more like default.
It’s just so easy to do that I don’t think many teams consider what the best choice is. They just ingest the data and deal with it later.
Plus I will add, that the tools tend to just work.
Access to modern ELT tools, such as Estuary, provides automated, pre-built data connectors that help eliminate the need to build custom ingestion pipelines. These tools, combined with dbt (data build tool), help teams shift from labor-intensive ETL to the easier, more flexible alternative, ELT.
The Real Problem Today
Now as much as some teams want to argue the semantics about ELT vs ETL. I think this is often a distraction from actually delivering. Instead of ETL vs. ELT, teams should ask the following questions to improve outcomes.
To be clear, you should make conscious and clear design decisions. But if you’re spending four weeks debating this topic, that’s too long.
Are We Modeling Data Correctly?
Today, data teams are focusing less on ELT vs. ETL and more on data quality, usability, and speed to insight. Modern pipelines have become too complex. This complexity is slowing the generation of usable insights. Consider the following:
- Over-aggregated tables vs. flexible models: There are concerns surrounding inflexible, over-aggregated tables, which is why a hybrid approach is ideal. Having raw data available while modeling the most critical business metrics using a star schema.
- Supporting exploration vs. just dashboards: Instead of leaning on just traditional dashboards, build exploratory data environments. Analysts and business users can discover the insights they need without waiting for new reports.
- AI/LLM implications: There is a shift from code-heavy ETL pipelines to semantic layers. These layers provide context for artificial intelligence (AI). This approach provides a single source of truth for humans seeking BI insights and for Large Language Models (LLMs), enabling more accurate, AI-driven analysis.
Are We Managing Costs?
Rather than focusing on the ETL vs. ELT debate, teams should prioritize operational efficiency and cost management. The core issue is not the framework but more about how it is implemented.
ELT is often favored because of its speed and flexibility. However, the cost of transformation shifts to cloud warehouse bills. Computing costs and wasted resources can quickly add up, especially when teams over-transform or use suboptimal scheduling.
ETL involves higher predictable, upfront costs. However, it can hide inefficiencies that waste resources. For example, over-engineered pipelines are costly to maintain.
Are We Solving Business Problems?
Teams can build what seems like a perfect pipeline. However, if it fails to drive actual revenue or doesn’t support decision-making, what is the point? A perfect pipeline that has zero impact isn’t beneficial to anyone.
If metrics aren’t driving decisions, they become noise. Instead of just building pipelines, teams have to focus on actionable insights. The old question of how quickly data can be loaded (ETL vs. ELT) has shifted to how quickly the business can make decisions based on data quality and availability. ELT offers faster access for data scientists and analysts. However, ETL provides compliant data for reporting.
What To Do Instead
Ok so if we’re going to stop arguing about ETL vs ELT, what should teams actually be doing?
Because saying “the debate doesn’t matter” without offering a path forward is its own kind of unhelpful.
Here’s what I’d push teams to focus on instead.
Start Pushing For Decisions
This sounds obvious but most teams don’t actually do it. They start with “we have data in Salesforce, Stripe, and Postgres, let’s get it into Snowflake.” Cool.
Then what? Six months later you have 400 tables and seventy dashboards no one looks at.
Instead, work backwards.
What decision is someone trying to make?
Who is making it?
How often?
What does “good enough” data look like for that decision?
Once again, design is important. But we, as data people are so often so deep in our debates about Iceberg vs this and ELT vs that. That we miss the bigger point.
Pick a Default and Move On
For most teams in 2026, if you’re stilling arguing about ELT vs ETL, you’re probably not delivering much. Not design decisions are important. You need to make them. But I find many companies prefer arguing for months on these details. Make a decision and move on.
I’ve personally seen both approaches work.
For smaller teams, you probably will benefit from moving faster and getting data into your data warehouse. So ELT can be great.
On the flipside, the teams that should genuinely consider ETL (or a hybrid) are the ones with real constraints, things like regulated industries where you can’t land raw PII in the warehouse, situations where source systems can’t tolerate the load of raw extracts, or cases where compute costs in the warehouse are genuinely getting out of hand.
If none of those apply to you, you’re probably overthinking it.
Invest in the Boring Stuff
The unsexy truth is that the teams delivering the most value aren’t the ones with the most modern architecture. They’re the ones who have thought through the boring stuff.
We’ve already talked about designing for the business and decisions.
But here are some other things worth thinking about.
- Naming conventions and consistent table/column standards (your future self will thank you)
- Testing pipelines like software, meaning unit tests, data quality tests, regression tests
- Documentation that actually gets updated, not just written once and abandoned
- Version control and code review for SQL and pipeline code (yes, even the “quick fix”)
- Data contracts between source systems and the warehouse so upstream changes don’t silently break everything
- Monitoring and alerting on freshness, volume, and schema drift, not just job success/failure
- Incident runbooks so the same person isn’t getting paged at 2am every time
- Clear ownership
None of this is about ETL vs ELT. All of it determines whether your data platform is useful or just expensive.
Become Outcome Obsessed
Number of pipelines built is not a metric. Number of dbt models is not a metric. Number of dashboards shipped is definitely not a metric.
What is a metric: did the marketing team change their spend allocation because of something we built?
Did finance close the books a day faster?
Did product kill a feature based on usage data?
If you can’t point to decisions that happened differently because of your data team’s work, the architecture debate is moot.
Success Goes Beyond This Traditional Debate
The debate between ETL and ELT is secondary to actual business outcomes. What matters most is impact. Companies don’t win because they picked ELT. They succeed by delivering reliable data, moving fast, and staying on budget. So, while ELT remains the modern standard, its value lies in how an organization uses it to achieve results.
If you’d like to read more about data engineering and data science, check out the articles below!
What Are Data Pipelines And Why Do They Exist
Cut Your Snowflake Bill by 70%
What Is A Data Platform And Why You Should Build One
Throughput vs Latency: Understanding the Key Difference in Data Engineering
How to build a data pipeline using Delta Lake