ETL vs ELT: Why the Market is Shifting Toward ELT

In the world of data engineering, the way we move and transform data has evolved dramatically. Two popular paradigms — ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) — define how modern data pipelines handle data. While both aim to prepare raw data for analytics and business insights, their approach, scalability, and ecosystem fit are quite different.

What is ETL?

ETL stands for Extract, Transform, Load.
It’s the traditional process where:

1. Extract – Data is pulled from source systems (like CRM, ERP, APIs, etc.).
2. Transform – Data is cleaned, aggregated, or formatted before it’s stored.
3. Load – The transformed data is loaded into a data warehouse or database.

This approach was ideal when compute resources were limited and on-premises databases had fixed capacity. The heavy transformation step was performed in dedicated ETL servers before data ever reached the warehouse.

🔹 What is ELT?

ELT flips the process:

1. Extract – Data is still pulled from source systems.
2. Load – Raw data is loaded directly into a modern data warehouse or data lake.
3. Transform – Transformations happen after loading, leveraging the power of cloud-based compute engines like Snowflake, Databricks, or BigQuery.

This model fits the cloud-native architecture where storage is cheap, compute is scalable, and parallel processing is easy to achieve.

Why the Shift Toward ELT?

The shift is driven by several technological and business factors:

1. Cloud Warehouses Are Powerful
   Modern data platforms can perform large-scale transformations efficiently using SQL or Spark.
2. Decoupling of Storage and Compute
   Cloud systems let you scale compute independently, making in-warehouse transformations cost-effective.
3. Faster Time-to-Insight
   Raw data can be made available instantly for exploration, while transformations run asynchronously.
4. Support for Semi-Structured Data
   ELT handles JSON, Parquet, and Avro formats natively — a big win for data from APIs and IoT devices.
5. Simplified Maintenance
   Fewer external ETL servers mean reduced complexity and better governance.
6. Integration with Modern Tools
   Tools like dbt (Data Build Tool) and Databricks Delta Live Tables (DLT) are designed specifically for ELT workflows.

ETL vs ELT: A Quick Comparison

Feature	ETL	ELT
Transformation Location	Outside the warehouse	Inside the warehouse
Performance	Limited by ETL server	Scales with cloud compute
Maintenance	Complex	Simpler
Data Freshness	Slower	Faster
Best For	On-prem or legacy systems	Cloud-native environments

💡 Real-World Example

A company using Salesforce and Google Analytics can use ELT to first load all raw data into Snowflake, and then apply transformations using dbt models. This enables analysts to test, modify, and version control SQL transformations directly in the warehouse — no separate ETL tool required.

🚀 The Future is ELT

The rise of data lakes, lakehouses, and real-time pipelines is making ELT the new standard. ETL will still exist for niche or legacy scenarios, but the industry trend clearly favors ELT-first architectures — especially for organizations embracing the Modern Data Stack.

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In summary:
ETL walked so ELT could run.
With cloud platforms, data engineering is no longer about moving data — it’s about empowering teams to transform insights faster.