Both acronyms describe moving data from a source system into a destination, through the same three operations: Extract (pull data from the source — a CRM, ad platform API, GA4 export), Transform (clean, reshape, join, aggregate), and Load (write into the destination). ETL transforms the data in a separate processing step *before* loading it into the warehouse — the warehouse only ever contains clean, modeled data. ELT loads the raw, untransformed data into the warehouse first, then transforms it *inside* the warehouse using its own compute (typically via SQL and a tool like dbt). The order flip isn't cosmetic — it changes where transformation logic lives, how it's versioned, and what happens when a transformation turns out to be wrong.

ETL vs. ELT

Dimension	ETL	ELT
Where transformation runs	Separate processing engine (Spark, custom scripts) before load	Inside the warehouse itself (BigQuery, Snowflake) via SQL
Raw data retained?	Often discarded or stored separately — not in the warehouse	Yes, always — raw data lands in the warehouse before transformation
Re-running a changed transformation	Requires re-extracting from source or reprocessing in the external engine	Just re-run the SQL model against already-loaded raw data
Tooling	Informatica, Talend, custom Python/Spark pipelines	Fivetran/Stitch/Airbyte for load + dbt for transform
Modern default for marketing stacks	Less common now	Dominant pattern since cloud warehouses made in-warehouse compute cheap

Why ELT became the default for marketing data specifically

Marketing data has two properties that make ELT especially well suited to it: source schemas change frequently and without warning (an ad platform adds a new field to its API response, a CRM admin renames a custom field), and marketing teams often don't know in advance exactly which transformations they'll need — the same raw ad spend data might need to be modeled differently for a channel-performance dashboard versus an LTV model versus a finance reconciliation report. ELT's 'load raw first, decide transformations later' approach means a schema change doesn't break the load step, and a new transformation need doesn't require re-extracting from the source — it's just a new SQL model against data that's already sitting in the warehouse.

elt-transform-in-dbt.sql

sql

-- In an ELT pipeline, this transformation runs AS A dbt model, inside
-- the warehouse, against raw data that Fivetran/Airbyte already loaded
-- untransformed. If the logic is wrong, fix the SQL and re-run - no
-- re-extraction from the ad platform API needed.

-- models/marts/channel_spend_unified.sql
SELECT
  'meta' AS channel,
  date,
  campaign_name,
  SUM(spend) AS spend,
  SUM(impressions) AS impressions,
  SUM(clicks) AS clicks
FROM {{ source('raw_meta_ads', 'campaign_insights') }}
GROUP BY date, campaign_name

UNION ALL

SELECT
  'google_ads' AS channel,
  date,
  campaign_name,
  SUM(cost_micros) / 1000000 AS spend,
  SUM(impressions) AS impressions,
  SUM(clicks) AS clicks
FROM {{ source('raw_google_ads', 'campaign_stats') }}
GROUP BY date, campaign_name

Keeping raw data in the warehouse is itself the main architectural benefit

Beyond flexibility, ELT's habit of preserving untransformed raw data inside the warehouse means a bad transformation is always recoverable — you can inspect the raw source data directly to debug a discrepancy, and reprocessing a corrected transformation never requires going back to the original source system (which might have rate limits, a limited lookback window, or simply no longer have the historical data available via its API).

ELT shifts cost and governance risk, it doesn't eliminate them

ELT trades upfront transformation engineering cost for ongoing warehouse compute cost, since every transformation now runs as warehouse queries rather than in a separate (often cheaper) processing engine — on large data volumes, poorly optimized dbt models can meaningfully inflate a BigQuery or Snowflake bill. It also means raw, potentially sensitive data (PII in a CRM export, for instance) lands in the warehouse before any masking or access control is applied, which raises governance considerations ETL's pre-load transformation step would have handled earlier.

What's Next?

dbt has become the dominant tool for the transformation step in modern ELT marketing data stacks — understanding how it structures and version-controls that transformation logic is the natural next step.

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