ETL vs ELT for marketing data: which fits?
ETL transforms data before loading it, ELT loads it raw first. How the two differ for marketing data, when each fits, and what to choose.
The short answer: ETL transforms data before loading it into a warehouse. ELT loads the raw data first and transforms it inside the warehouse. For marketing data, ELT is usually the better fit, because ad platforms change their fields and definitions, and with the raw data kept you can remodel without pulling it all again.
The difference
- ETL (extract, transform, load) pulls data from a source, cleans and reshapes it in a separate step, then loads the finished result into the warehouse.
- ELT (extract, load, transform) pulls the data and loads it as it is, then reshapes it inside the warehouse, using the warehouse's own computing power.
Both end in the same place: tables your reports and dashboards can trust. The difference is when the decisions about structure get made, and whether you can change your mind later.
Side by side
| ETL | ELT | |
|---|---|---|
| Order | Extract, transform, load | Extract, load, transform |
| Where the transforming happens | In a separate step, before the warehouse | Inside the warehouse, usually in SQL |
| Raw data kept | Often not | Yes, as a first layer |
| Changing a definition | Change the job, then re-pull the data | Change the model and rebuild from raw |
| Personal data | Can be removed before it lands | Lands first, so access controls matter |
Why ELT usually suits marketing data
This is a judgement from building these systems, not a rule:
- Sources change. Google Ads, Meta, LinkedIn and GA4 rename fields, change defaults and restate numbers. If you kept the raw data, you can update the model and rebuild. If you transformed before loading, you may have to pull history again, and some platforms limit how far back you can go.
- The questions change. Today you want spend by channel. Next quarter you want spend joined to CRM stages and click IDs. Raw data lets you answer the new question without a new pipeline.
- Transforming in SQL is easier to check. Analysts and marketers can read the model, and a wrong definition is one change in one place.
- Warehouses are built for it. Joining large tables is what they do well.
The trade-off: raw data can include personal data such as names and email addresses from the CRM, and a faulty load can pass problems downstream. That needs access control and a sensible rule about what lands.
When ETL still makes sense
- You must remove or hash personal data before it reaches the warehouse.
- The data is small and the output is fixed, such as a simple monthly extract.
- A rule requires filtering at the source.
- You are working with older tools built that way.
Many real systems mix the two: light cleaning on the way in, and the heavy modelling afterwards in the warehouse.
A marketing example
- Extract and load: ad spend and clicks from Google, Meta, LinkedIn and Bing, sessions and events from GA4, and leads and deal stages from the CRM, each landing as raw tables with a load date.
- Transform: one table of spend by day and channel; one of leads by source with UTMs and click IDs; one that joins leads to deal stages and revenue. This is where your attribution rules and stage definitions live.
- Serve: a dashboard that reads the modelled tables, and lists of qualified leads that can be sent back to ad platforms as conversions. See Google Ads offline conversions from your CRM.
Most of the real work is in the middle step: agreeing what a lead is, which source wins, and how stages map. The pipe that moves the data is the easy part. See what a marketing data warehouse is and B2B marketing attribution.
Cost and upkeep
- Connectors. Managed connectors save build time but are priced by volume or seats. See Supermetrics alternatives.
- Warehouse compute. ELT shifts transformation cost into the warehouse, so how often you rebuild, and how much each rebuild scans, matters.
- Monitoring. Whichever you choose, syncs fail quietly. Someone has to watch them.
- Reporting layer. A free report tool can sit on top. See Looker Studio vs a data warehouse.
We build these in the client's own warehouse or host them, land raw data first, model on top, and monitor the nightly syncs. See our Data and BI work, or start with a free data audit to see what you have.
Questions people ask
What is ETL?
Extract, transform, load: data is pulled from its source, cleaned and reshaped in a separate step, and then loaded into the warehouse in its final form.
What is ELT?
Extract, load, transform: data is loaded into the warehouse as it is, then transformed inside the warehouse.
Is ELT better than ETL for marketing data?
Usually, because ad platforms change their fields and definitions and the raw data lets you remodel without pulling everything again. ETL still makes sense when personal data must be removed before it lands.
Do I need either if I only use Looker Studio?
Not necessarily. With a few channels and platform numbers that are good enough, a connector straight into a report tool can be enough. A pipeline and warehouse become worth it when you need to reconcile platforms against your CRM or keep your own history.
What is reverse ETL?
Copying modelled data from the warehouse back into operational tools, such as sending qualified leads to ad platforms as conversions or syncing audiences to a CRM.
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