The Growing Shift From ETL to ELT in Modern Data Engineering

2026-05-31 · Source: Data Engineering on Medium · Field: Technology & Digital — Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Intermediate, quick

Summary

Modern data engineering architectures are increasingly shifting from traditional ETL (Extract, Transform, Load) to ELT (Extract, Load, Transform) workflows. This transition is primarily driven by the capabilities of modern cloud platforms and distributed processing. Historically, ETL pipelines transformed data externally before loading it into target systems. However, the ELT approach now allows organizations to load raw data directly into scalable cloud platforms first, performing transformations later within the platform itself. This method offers greater flexibility, particularly for analytics teams requiring immediate access to raw datasets. It also enables engineers to utilize scalable compute engines for in-place processing, often reducing overall pipeline complexity compared to repeated external transformations. This evolution reflects a broader cloud-native thinking influencing contemporary data pipeline design.

Key takeaway

For Data Engineers designing new data pipelines or optimizing existing ones, consider adopting an ELT approach over traditional ETL. If your organization uses scalable cloud platforms, loading raw data directly into the platform before transformation can significantly increase flexibility for analytics teams and reduce pipeline complexity. Evaluate your current data processing architecture to identify opportunities for utilizing cloud-native compute for in-platform transformations, potentially streamlining your data workflows and improving data accessibility.

Key insights

Modern cloud platforms enable ELT workflows, loading raw data first for flexible, in-platform transformation.

Principles

• Cloud scalability drives ELT adoption.
• In-platform transformation enhances flexibility.
• Distributed processing reduces pipeline complexity.

Method

The ELT method involves extracting data, loading it raw into a scalable cloud platform, and then transforming it directly within that platform using its compute engines.

In practice

• Load raw data directly into cloud storage.
• Utilize cloud-native compute for transformations.
• Provide analytics teams raw data access.

Topics

• Data Engineering
• ELT Workflows
• Cloud Platforms
• Data Transformation
• Distributed Processing
• Data Pipelines

Best for: Data Engineer, Analytics Engineer, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Engineering on Medium.