What are common misconceptions about ELT?

Common misconceptions include: ELT is always better than ETL. Actually, ETL is still preferred when data must be cleaned before loading or when the destination lacks transformation capability. Also, ELT is just ETL with the steps reordered. Actually, ELT represents a fundamentally different architecture that leverages modern warehouse capabilities..

Glossary

What is ELT?

Extract, Load, Transform — a modern data integration approach that loads raw data into the destination first, then transforms it using the destination's processing power.

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In Depth

Understanding the Details

ELT inverts the traditional ETL process by loading raw data into a modern data warehouse or lake before transforming it. This approach leverages the massive processing power of cloud warehouses like Snowflake and BigQuery, which can transform large datasets efficiently. ELT preserves raw data in its original form, allowing analysts to create new transformations without re-extracting from sources. Tools like Fivetran and Airbyte handle the Extract-Load portion, while dbt (data build tool) has become the standard for the Transform step. ELT is generally preferred for analytics use cases where transformation requirements evolve, while traditional ETL remains better when data must be cleansed before entering the destination.

Examples

How It Works in Practice

Analytics data stack

Fivetran extracts data from 15 sources and loads it raw into BigQuery. dbt models then transform the data into clean, business-ready tables.

Flexible reporting

Because raw data is preserved, analysts can create new transformations for emerging questions without requesting re-extraction from source systems.

Modern data stack

An ELT pipeline with Airbyte (extract/load), Snowflake (warehouse), dbt (transform), and Looker (visualise) enables self-serve analytics.

Importance

Why It Matters

ELT enables faster data availability and more flexible analytics by decoupling extraction from transformation, letting teams iterate on data models without waiting for pipeline changes.

Misconceptions

What People Often Get Wrong

ELT is always better than ETL. Actually, ETL is still preferred when data must be cleaned before loading or when the destination lacks transformation capability.

ELT is just ETL with the steps reordered. Actually, ELT represents a fundamentally different architecture that leverages modern warehouse capabilities.

Raw data loading means no data governance. Actually, ELT can include governance through transformation layers and access controls in the warehouse.

Our Approach

How We Handle ELT

We design modern data stacks using ELT patterns where appropriate, with proper dbt transformations and data modelling that make warehouse data actually usable.