The difference is the order of the steps. With ETL (extract, transform, load), you clean and reshape data before it reaches its destination. With ELT (extract, load, transform), you copy the raw data into a cloud data warehouse first and reshape it there. For most small businesses that already use cloud tools, ELT is the better default today. ETL still earns its place in a few specific situations, covered below.
We build connected software for small and medium businesses in Surrey and Metro Vancouver, so we see this question mostly from owners and operations managers. Their CRM, invoicing and scheduling tools don't talk to each other, and someone has asked whether they need "a pipeline." This guide is written for that person, not for a data engineer.
Table of Contents
- What is the difference between ETL and ELT?
- What is ELT, in plain terms?
- ETL vs ELT: how do they compare side by side?
- Is ELT always better than ETL?
- Is Snowflake, dbt or a data lake ETL or ELT?
- What does ELT look like for a business with 10 to 50 people?
- How do you avoid the "data swamp" with ELT?
- Do you need ETL or ELT at all?
- Perspective: why we often build one system instead of a pipeline
- How can we help you decide?
- FAQ
- Sources
What is the difference between ETL and ELT?
Both patterns do the same three jobs, in a different order.
- Extract: pull data out of a source such as your CRM, accounting package or booking system.
- Transform: clean it, fix formats, remove duplicates, match customers across systems and calculate things like revenue per client.
- Load: put the result somewhere reporting can use it.
In ETL, step 2 happens on a separate processing server, before loading. In ELT, the raw data lands in the warehouse first, and the warehouse's own computing power does the transforming. Snowflake describes ETL as the traditional approach for on-premises systems with limited storage, and ELT as the pattern that suits cloud-native environments. Its documentation also notes that the newer term ELT stresses that transformation doesn't have to come before loading.
An analogy, used sparingly: ETL is like sorting and labelling your receipts before they go in the filing cabinet. ELT is putting every receipt in the cabinet first and sorting them when someone asks a question. The second way is quicker up front. It only works if you keep the cabinet organized.
What is ELT, in plain terms?
ELT means you move data first and decide what to do with it later. A tool copies records from your business apps into a cloud warehouse, as they are. Then you write transformations, usually in SQL (the standard language for querying data), that turn the raw tables into clean, report-ready ones.
dbt Labs explains that ETL was long the standard way to move data from many sources into one place, and that cloud infrastructure and growing data complexity pushed teams toward ELT. Two things made that possible: storage became cheap, and cloud warehouses became powerful enough to do heavy transformation themselves.
For a small business, this matters in three ways:
- You keep the raw data. If you decide next year that you want to measure repeat customers differently, you can re-run the logic on the original records. In classic ETL, data is often aggregated or discarded before storage, so you can't go back. DSC, a Canadian data engineering firm, lists this as a major advantage when analytics needs change.
- Less up-front design. You don't have to settle every reporting question before you start moving data.
- People who know SQL can do the work. Snowflake's own best-practice documentation describes how dbt projects remove infrastructure you would otherwise manage yourself, so a small team doesn't need deep infrastructure skills.
ETL vs ELT: how do they compare side by side?
| ETL | ELT | |
|---|---|---|
| Where transformation happens | On a separate processing server, before loading | Inside the cloud warehouse, after loading |
| Raw data kept? | Often not | Yes, by design |
| Best fit | Strict rules about what may be stored, older on-premises systems, small and well-defined datasets | Cloud tools, changing reporting needs, mixed data types |
| Changing a report later | Usually means changing the pipeline | Often means editing a SQL model and re-running it |
| Main risk | Rigid and slow to change | A messy "data swamp" if nobody documents or tests |
| Skills needed | Pipeline tooling or scripting | SQL plus good habits (version control, testing) |
Two cautions about common claims. First, you will see pages say ELT is always faster and cheaper. It often is at scale, but not automatically. The warehouse bills you for the computing it uses, so careless transformations cost real money. Second, the Reddit discussion that ranks for this keyword makes a fair point from the other side: ETL can be easier to unit test, because the transformation is separate code. ELT can match that, but only when you adopt tools and discipline for testing SQL.
Is ELT always better than ETL?
No. We would pick ETL in a few cases:
- Data you must not store raw. If a source contains sensitive personal information that you don't want sitting in a warehouse unmasked, clean or strip it before loading. That is an ETL step, or a hybrid. Our plain guide on whether your business data is safe with AI tools covers the privacy side in more detail.
- A single, stable data flow. If you only ever need one report from one system, and it won't change, a simple scheduled script is fine. You don't need a warehouse.
- An existing on-premises setup that works. Rebuilding something that runs reliably, just to follow a trend, is a poor use of money.
Many real systems are hybrids. A small amount of cleaning happens before loading (removing fields you shouldn't store), and the rest happens in the warehouse. Don't let the labels push you into a purist position.
Is Snowflake, dbt or a data lake ETL or ELT?
These come up a lot, so briefly:
- Snowflake, BigQuery and Amazon Redshift are cloud warehouses. They are where ELT transformations run. Snowflake's material names cloud platforms like these as the typical home for ELT. A warehouse itself is neither ETL nor ELT. It is the place the pattern runs.
- dbt (data build tool) is the "T" in ELT. It manages the transformations inside the warehouse as version-controlled, testable code. The Snowflake documentation on dbt projects is a good reference.
- A data lake is storage for raw files and records. It fits the "load first" idea of ELT, but a lake alone doesn't transform anything. If you are weighing a lake against a warehouse, we wrote a separate piece on data lake vs warehouse for SMBs.
What does ELT look like for a business with 10 to 50 people?
Picture a Surrey home-services company. Jobs are booked in a scheduling app, quotes live in a CRM and invoices go out from accounting software. The owner wants one weekly view of revenue by job type, and the bookkeeper spends hours every Friday copying numbers into a spreadsheet.
An ELT setup for this business might work like this:
- A connector copies jobs, quotes and invoices into a cloud warehouse every night, untouched.
- SQL models match each invoice to its job and customer, and standardize job-type names.
- A dashboard reads the clean tables, so Friday's spreadsheet work disappears.
- When the owner later asks about repeat customers, someone adds a model. Nothing needs to be re-extracted.
The point is that the bookkeeping hours move into software. Our clients typically report saving 5 to 15 hours of administrative work a week after we connect their systems. That figure comes from our own client work, and your result will depend on how manual your processes are today.
There is a leaner way to see the same idea. Lean teams in other industries consolidate their business apps this way too: a restaurant platform like RESTOBOT's CRM and analytics dashboard shows what it looks like when customer, order and reservation data sit in one reporting view, with no one retyping numbers.
How do you avoid the "data swamp" with ELT?
The easy start is also ELT's trap. Because you can load everything without designing anything, it is tempting to do exactly that. Snowflake's guidance is to treat transformation code with the same rigour as software code, with version control and clear documentation from early on. In practice, that means:
- Start with one or two sources. Snowflake's developer guide walks through a build-up approach. We usually suggest your CRM first, then invoicing. A single all-encompassing pipeline for every system at once is a classic way for these projects to stall.
- Name things clearly and write down what they mean. "Revenue" should mean one thing everywhere.
- Add tests. Check that invoice totals aren't negative, that every invoice has a customer, that yesterday's data arrived.
- Make failures loud. DSC makes the point that the specific tool matters less than the pattern, and that a pipeline should fail visibly and recover cleanly rather than rely on someone noticing a wrong number.
- Assign an owner. Someone in the business should be responsible for what the numbers mean, even if an outside team maintains the plumbing.
Do you need ETL or ELT at all?
Sometimes the honest answer is no. A warehouse and pipeline are worth it when you have three or more systems holding related data, when reporting means manual copy and paste, or when you plan to use AI tools on your own business data.
That last point is worth a moment. The National Research Council of Canada's Data Analytics Centre is staffed by researchers in AI, data science and machine learning. Its work reflects a common finding: the hard part of AI is rarely the algorithm. It is getting data that is accessible and reliable. The federal government says much the same in its 2023-26 IRCC Data Strategy, which describes integrating multiple lines of business as foundational for analytics and predictive products. A small business has a smaller version of the same problem. A chatbot or forecasting tool is only as good as the records behind it.
If you have one tool and a tidy spreadsheet, skip the pipeline. If your data is scattered but small, there is a third path, which we describe next.
Perspective: why we often build one system instead of a pipeline
We build custom software that puts CRM, invoicing, scheduling, reporting and workflow automation behind one login. That shapes how we answer the ETL vs ELT question.
For many small businesses, the pipeline is a repair for a problem that started earlier: the tools were never connected. You can copy data from five systems into a warehouse and report on it well. But you are still paying for five subscriptions, keeping five sets of records and reconciling five opinions about who a customer is. If the business is small enough, it can be cheaper over time to fix the root cause. One system, one customer record, no pipeline to maintain.
That is not the right answer for everyone. If you rely on a specialist industry tool you love, or you have years of history in several systems, a warehouse with ELT is a sensible way to get a combined view without replacing anything. In those cases we would still start small, with your CRM and one other source, and build outwards. We would also treat the transformation logic as an asset you own, not something locked in a vendor's dashboard. Clients own the code we write, with no lock-in.
We would also warn against hype. A pipeline will not make your data accurate if people enter it inconsistently. Fix the entry habits at the same time as the plumbing, and train the team on why it matters. That training is part of what we do.
— Harry Gill
How can we help you decide?
If you're unsure whether you need a pipeline, a single system or neither, start with a conversation about what you report on and where the numbers live today. We offer data analysis and AI reporting for businesses in Surrey and Metro Vancouver, and our custom software builds are fixed-price with regular milestones. Our AI strategy and discovery services are a lower-commitment place to start. Pricing is scoped per project, so we don't publish a figure here.
For a deeper look at the planning side, read our data integration guide for Canadian SMBs.
FAQ
Is ETL or ELT better for a small business?
ELT is usually the better default if your tools are cloud-based and your reporting questions will change. You keep the raw data and can rework the logic later. ETL is the better choice when you must clean or remove sensitive fields before storage, or when your data flow is simple and fixed.
Will ETL be replaced by AI?
Not in a way that removes the need to plan your data. AI tools can help write transformation code and spot errors, but they still depend on clean, connected source data. The data integration work does not go away. It becomes the foundation that AI tools rely on.
Do I need a data warehouse for ELT?
Yes, ELT assumes a destination that can transform data itself, usually a cloud warehouse. If you have only two systems and light reporting needs, a warehouse may be more than you need. A connected custom system or a simple integration can be enough.
How long does it take to set up ELT?
It depends on the number of sources and how messy the data is. DSC says a Snowflake-based analytics foundation can often be set up in under two months, with value delivered step by step. For a small business starting with one or two sources, a first working report should come sooner than a full build.
Sources
- Moving from On-Premises ETL to Cloud-Driven ELT, Snowflake
- Understanding ELT: extract, load, transform, dbt Labs
- Best practices for dbt Projects on Snowflake
- Accelerating Data Teams with dbt Cloud & Snowflake
- Data Engineering Services, DSC
- Data Analytics Centre, National Research Council Canada
- 2023-26 IRCC Data Strategy, Government of Canada