ETL vs. ELT

Sooner or later, every system’s telemetry flows into a data pipeline. Scattered logs might help you debug locally, but scalable decisions depend on well‑structured, trustworthy pipelines.

That’s why Staff+ engineers must understand the trade‑offs between the two dominant pipeline patterns: ETL and ELT.

This choice shapes how fast teams can experiment, how expensive analytics become, and how transparent your systems are to the rest of the org.

This lesson will explain both approaches and help you choose the right one for your stage of growth.

ETL vs. ELT

Each approach starts with extracted data, but differs in when and where that data is transformed:

• ETL: Extract → Transform → Load

  • You extract the data, transform it before it reaches the warehouse, and then load it in.

• ELT: Extract → Load → Transform

  • Load the raw data first, then transform it inside the warehouse.

At a glance, ETL and ELT differ only in the order of operations. But that simple shift has major implications.

• In ETL, transformations happen before loading.

  • That means you need to know upfront what the data should look like — making it harder to change later, but cheaper and cleaner once it lands.

• In ELT, you load first, then transform inside the warehouse.

  • This makes exploring and evolving easier, but can lead to messy warehouses and higher costs if not managed well.

That single ordering difference shapes the entire data workflow—from how fast you can answer questions to how much infrastructure you need to maintain.

Here’s a side-by-side breakdown of the trade-offs:

How to choose an approach

There’s no one right answer—both approaches have trade-offs. The right choice depends on the shape of your product, the needs of your business, and how fast things are changing.

Let’s break it down.

Choose ETL when:

• Stability > speed: Your product is mature, and data needs are well-defined.

• Data is sensitive or regulated: You must sanitize or standardize before storage.

• You want tight control over schema and quality: All transformations happen before the data lands in the warehouse.

• You want to minimize warehouse costs: You're storing less data and doing less compute downstream.

Choose ELT when:

• Speed > polish: The product is evolving, and your team needs to ask new questions often.

• You want analysts to work with raw data: Useful for exploring ideas without re-ingesting data.

• You use modern tools (like Snowflakehttps://www.snowflake.com/en/ or BigQuery): Handles transformation at scale.

• You’re optimizing for team velocity: Engineers and analysts can iterate quickly without upstream rework.

Staff+ engineers choose based on what unblocks their team and aligns with the business. That might mean starting with ELT to move fast, then maturing into ETL as the system hardens.

Of course, John he prefers ETL because he’s the only one who knows where the transformation scripts reside. But you’ll take a different path.

Whether you use ETL or ELT, you’ll document transformations, build them in shared tools, and make pipelines transparent. That’s how you turn pipelines into leverage.

When product velocity is high, ELT may empower analysts and engineers to move fast. When data quality, compliance, or cost control are paramount, ETL might be the right tool for the job.

Example: User sign-ups

Imagine you want to analyze signup behavior.

• ETL approach: As data leaves the app, you strip out unused fields, normalize names, and reshape it before loading only clean signup records into the warehouse. The data is tidy but rigid—if the PM later asks to analyze a field you dropped, you’re stuck.

• ELT approach: You load raw signup events straight into the warehouse. Later, you transform them with SQL or dbt models into curated tables for analysis. It’s messy at first, but it lets you quickly answer new questions.

Neither solution is perfect.

You might choose:

• ETL if signups are tightly regulated (e.g., compliance rules mean you must sanitize data before storage), or if your transformations are stable and unlikely to change.

• ELT if the product evolves quickly, requirements change often, or the business values experimentation and flexibility over perfect cleanliness.

Staff+ engineers know when to choose which, and they make the choice transparent to the team.