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What is dbt and why is it central to modern ELT?

dbt is a tool that turns in-warehouse SQL into version-controlled, tested, documented models, and it is central to ELT because it runs the transforms inside the warehouse next to the data. Because a dbt model reads from named upstream models, dbt knows the dependency order and can rebuild a feature table byte for byte whenever the raw data updates, and that same dependency graph gives you tests, docs, and lineage for free.

What does pushing the transform down to the data mean?

Pushing the transform down means running the transformation as SQL inside the warehouse, on the same machines that store the data, instead of pulling gigabytes out to a separate cluster and shipping results back. Nothing moves across the network, and the warehouse engine, which is very good at scanning and joining large tables, does the work in place, which is what makes ELT cheap and avoids a second compute system to own.

Does ELT get rid of Spark?

No, ELT does not delete Spark, it moves Spark to the jobs that genuinely need distributed non-SQL compute, such as parsing raw images, billion-row custom joins, and Python-heavy preprocessing. Routine cleaning, joins, and aggregations become warehouse SQL run through dbt, and only the genuinely heavy work stays on Spark.

Why does keeping raw data forever matter for machine learning?

Keeping raw data forever matters because the feature a future model needs is often one nobody knew they wanted when the data first arrived. ELT loads everything raw and keeps the full history, so when the ML team later asks for order timestamps or individual line items, the data is already there instead of having been thrown away by a transform-first pipeline. An immutable raw layer also lets you rebuild a training dataset from months ago exactly, which is essential for debugging model drift.

How does ELT change data freshness compared to nightly ETL?

A nightly ETL job couples freshness to how often you can afford to run the whole transform cluster, so it tends to settle at once a night and leaves the day stale. ELT loads raw continuously, so the transform cadence becomes a choice: run dbt every few minutes for minutes-behind freshness, or add change data capture and incremental models for seconds-behind freshness, without re-architecting the pipeline.

How do you handle sensitive data like PII in an ELT stack?

When regulation like GDPR or HIPAA forbids storing raw personal data even briefly, you tokenize, mask, or drop the sensitive fields on a compute layer before the data lands, which is ETL. This is the sharpest case where transform-then-load still wins, because loading raw and cleaning later would leave the raw values sitting in the warehouse in the meantime. Most teams run this as a hybrid: ELT for the bulk with a small ETL job to strip the regulated fields.

What is the core difference between ETL and ELT?

The order of the transform step: ETL transforms before loading, ELT loads raw first and transforms in the warehouse, ETL and ELT are the same three steps in a different order. ETL transforms on a separate system before loading only the result. ELT loads raw data into the warehouse first and transforms it in place with the warehouse's own engine.

Why did ELT become the default for analytics and ML in modern data stacks?

Because cheap cloud storage let teams keep raw data, and cloud warehouses split storage from compute so in-place transforms became cheap and elastic, Two prices collapsed. Cheap object storage (S3) made keeping raw data affordable, and cloud warehouses like Snowflake and BigQuery split storage from compute so transforming inside the warehouse became fast and cheap. dbt then made those SQL transforms version-controlled code.

In the medallion architecture, why is a bug in a silver or gold model cheap to fix?

Because bronze is immutable raw, so you fix the SQL and rerun against it with no re-extract from source systems, Bronze holds the raw exactly as it arrived and never changes. A bug in a silver or gold transform is fixed by editing the SQL and rerunning it against the unchanged bronze layer, with no fragile re-extract from source. That is why a training dataset from months ago can be rebuilt exactly.

Which situation is a genuine reason to still use ETL instead of ELT?

Regulation forbids storing raw PII, so sensitive fields must be masked or dropped before the data lands, When compliance rules like GDPR or HIPAA forbid storing raw personal data even briefly, you must transform (tokenize, mask, or drop the sensitive fields) before load. That is transform-then-load, which is ETL. Heavy non-SQL compute on Spark is the other common ETL case.

ETL vs ELT for ML: Why Load-Then-Transform Won

Name: System Design Masterclass
Availability: InStock

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