How ETL works
The three letters are the three stages. Extract pulls data out of each source — Google Ads, Meta Ads, GA4, Search Console, your CRM, your ecommerce platform — usually through each platform’s API on a schedule. Transform reshapes it so the pieces fit together: matching date formats, converting currencies, renaming a field that one platform calls a conversion and another calls a result, and deciding which of several similar metrics you will actually use. Load writes the result into one place, typically a data warehouse such as BigQuery, from which reporting tools read.
You will also see ELT, where data is loaded first in its raw form and transformed afterwards inside the warehouse. The distinction matters mostly to whoever builds it; the useful idea is the same either way. Raw copies are kept, and the tidying is a step you can inspect and correct rather than something that happens invisibly inside a chart.
Why ETL matters
Every advertising platform reports on itself, in its own definitions, over its own attribution window. Read them side by side in a spreadsheet and the totals will not agree, because they were never measuring the same thing. Pulling everything into one store with agreed definitions is what makes a genuine cross-channel view possible.
The second reason is history. Platforms restate figures, change their default attribution and limit how far back their interfaces go. A warehouse holds what was reported at the time, which is the only way to answer questions about last year with any confidence.
The third is time. Where reporting is assembled by hand each month, most of the effort goes into fetching and pasting rather than thinking, and the analysis is thin because the person doing it has run out of hours.
Common mistakes with ETL
The biggest is building the pipeline before agreeing the definitions. If nobody has decided what counts as a lead, moving the numbers faster just produces disagreement faster. The transform step is where those decisions live, and undocumented decisions become folklore nobody can explain a year later.
The second is transforming away detail. Data aggregated to a monthly total at load time cannot answer a question about a single campaign later, and the original may no longer be available from the platform. Keep the raw extract.
The third is silent failure. A scheduled job that stops running does not usually announce itself; the dashboard simply shows a flat line that looks like a quiet week. And most small businesses do not need this at all — a warehouse built for two ad accounts and one website is machinery around a problem that a well-built reporting dashboard already solves.
How to act on it
Start from the question, not the tooling. Write down the handful of things the business needs to know each month, and see whether existing connectors into a reporting tool already answer them. If they do, stop there.
If they do not — several markets, several currencies, offline sales that must be joined to online spend — then define your metrics in writing first, keep the raw extracts, schedule the loads, and monitor them. Build the smallest pipeline that answers today’s questions, into a data warehouse you can extend. Once that data is sitting there, reverse ETL is what pushes the useful parts back out to the teams and platforms that need them.