ETL vs ELT

2026-04-02 · Source: Alex The Analyst · Field: Technology & Digital — Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Intermediate, quick

Summary

The traditional Extract, Transform, Load (ETL) data integration paradigm, which involves transforming data on a small server before loading it into a database, is being superseded by the Extract, Load, Transform (ELT) approach in modern companies. ELT processes raw data directly into scalable systems like Databricks, Snowflake, and BigQuery, leveraging the current affordability and scalability of compute resources. This shift allows for post-load transformation, offering improved performance and direct access to raw data for subsequent modifications. The author notes a personal preference for ELT over the past year due to these benefits, despite a long-standing familiarity with ETL.

Key takeaway

For data architects and engineers designing new data pipelines, adopting an ELT strategy is now the preferred approach. This allows you to capitalize on scalable cloud compute resources, reduce initial transformation bottlenecks, and maintain access to raw data for future analytical needs or schema changes, ultimately improving performance and flexibility.

Key insights

Modern data integration favors ELT over ETL due to cheap, scalable compute and direct raw data access.

Principles

• Compute cost drives data pipeline architecture.
• Raw data access enhances flexibility.

Method

ELT involves extracting data, loading it into a scalable system (e.g., Databricks, Snowflake, BigQuery), and then performing transformations directly within that system.

In practice

• Load raw data directly into cloud data warehouses.
• Transform data post-load for flexibility.

Topics

• ETL
• ELT
• Data Integration
• Data Transformation
• Cloud Data Platforms

Best for: CTO, VP of Engineering/Data, Data Engineer, Analytics Engineer

Related on AIssential

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• What is data pipeline architecture? — Data pipeline architecture is the blueprint for data flow, defined by layers, stages, and patterns tailored to use cases.
• Main Data Warehouse Components Explained (2025) — Data warehouses organize vast datasets into actionable insights using a layered architecture and dedicated processing.
• I Thought Data Engineering Was Just Writing Scripts. I Was Wrong. — True data engineering extends beyond scripting to build reliable systems through idempotency, persistence, and scheduling.
• Learn ETL Pipelines in Databricks in Under 1 Hour | Data Engineering in Databricks — Databricks facilitates end-to-end ELT pipelines using Medallion Architecture, AI-assisted transformations, and automated job orchestration.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Alex The Analyst.