ETL or ELT?

About two months ago, during a project delivery discussion with a client, we were aligning on architecture, delivery, and timelines. The topic of data integration came up, and we hit a roadblock.

"Should we go for ETL or ELT?"

The question sparked a 30-minute debate. And this was a room filled with experienced senior developers and leads.

Yet there was confusion. Not because they lacked expertise, but because the context was missing.

Here’s what I’ve learned after witnessing (and resolving) this discussion multiple times across different clients and domains:

ETL vs ELT — The Core Difference

ETL (Extract, Transform, Load): You extract data from sources, transform it before it enters the data warehouse, and then load the clean data.

ELT (Extract, Load, Transform): You extract and load raw data first, then transform it within the data warehouse.

The distinction may seem small, but the implications are massive.

When Should You Use ETL?

ETL works best when:

• You're using traditional data warehouses (e.g., Oracle, SQL Server)
• There are strict transformation rules to meet before the data is stored
• You have limited compute power in your data warehouse
• Your data sources are well-structured and predictable
• Compliance requires sensitive data to be transformed before loading

Use Case Example: A banking client needed to mask PII before storing anything in their warehouse. ETL was the only secure choice. Data was transformed using on-prem middleware, then loaded into a tightly controlled SQL-based warehouse.

When Should You Use ELT?

ELT shines when:

• You're using modern cloud data platforms (e.g., Snowflake, BigQuery, Redshift)
• The data volume is high and comes from diverse sources
• You want to store raw data for future reprocessing
• You leverage on-demand, scalable compute
• You need to iterate on transformations post-load

Use Case Example: A retail client needed real-time marketing insights from clickstream data. With ELT, they loaded raw JSON logs into BigQuery and transformed them into usable formats via SQL views, iterating as new analytics needs emerged.

The Hidden Factor: Team Maturity

This is the part most project teams overlook.

→ ETL requires stronger DevOps & pipeline management. → ELT demands more data warehouse knowledge and SQL transformation skills.

If your team is stronger in Python/Java + DevOps, ETL might be more comfortable. If your team is cloud-savvy and fluent in SQL-based modelling, ELT is more agile and scalable.

Choosing the right approach is not just a tech decision - it’s a delivery decision.

Summary: ETL vs ELT Cheat Sheet

Final Thought

Before you choose a data integration strategy, ask:

• What are we solving for - speed, security, scalability, or flexibility?
• What is our team's maturity and skillset?
• What platform are we using today, and where do we want to go?

Don’t let acronyms drive your architecture.

Let context and capability lead the way.