ETL vs ELT: What's the Difference and Which Should You Use?

Every analytics setup has a plumbing problem. Data lives in your CRM, ecommerce store, accounting software and ad platforms, and it needs to end up somewhere central and clean enough to trust. The 2 standard ways to build that plumbing are ETL and ELT.

The letters are the same. The order is different, and that order changes where the work happens, what it costs, how fast you can answer new questions and how you protect sensitive data. This guide diagrams both flows, compares them side by side, explains why cloud warehouses moved most teams to ELT and shows when ETL is still the right call.

The difference in one sentence

Microsoft’s Azure architecture guide puts it simply: ELT differs from ETL solely in where the transformation takes place. In ETL, data is transformed by a separate engine before loading. In ELT, raw data is loaded first and transformed inside the target data store.

Both start with extract (copy data out of source systems) and both end with analysis-ready tables. The question is whether the transform step (cleaning, joining, deduplicating, reshaping) happens on the way in or after arrival.

How ETL and ELT work

How ETL works

Sources Transformation engine Data warehouse (CRM, POS, ERP) ---> clean, join, filter, ---> only curated, extract deduplicate, validate modeled tables (staging area) load

1. Extract data from each source system.
2. Transform it on a separate server or ETL tool, often using staging tables to hold data while it’s processed. Typical operations include filtering, sorting, aggregating, joining, cleaning, deduplicating and validating.
3. Load only the finished, structured result into the warehouse.

ETL dates back to the 1970s, according to AWS, and became the standard approach with on-premises data warehouses. Those systems had fixed, expensive compute, so it made sense to do heavy processing elsewhere and load only what reports needed.

How ELT works

Sources Data warehouse or lakehouse (CRM, POS, ERP) ---> raw tables ---> transformed, modeled tables extract load transform (SQL inside the warehouse)

1. Extract data from each source.
2. Load it as-is into raw tables in the warehouse or lakehouse.
3. Transform it inside the warehouse, usually with SQL, into clean models that BI tools and analysts use.

Because raw data is kept, you can build new models later without going back to the source. Tools such as dbt are built around this pattern: you write SQL select statements, and the tool turns them into tested, documented models that run directly in your cloud data platform.

ETL vs ELT comparison table

Why cloud warehouses shifted teams to ELT

The move to ELT wasn’t a fashion change. It followed the economics of cloud warehouses.

1. Compute became elastic. On-premises warehouses had a fixed amount of processing power, so wasting it on transformations was expensive. Cloud warehouses such as BigQuery, Snowflake and Redshift scale compute up and down on demand. Microsoft notes a direct benefit: scaling the target data store also scales ELT pipeline performance.

2. Storage got cheap. Keeping full raw copies of every source used to be costly. Cloud storage made it practical, and AWS points out that cloud platforms made it feasible to store large volumes of raw data and analyze it later as needed.

3. Fewer systems to run. ELT removes the separate transformation engine from the pipeline, which Microsoft says simplifies the architecture.

4. SQL became the transformation language. Analysts already know SQL. When transformations run as SQL inside the warehouse, analysts can own them, which spawned the “analytics engineer” role and tools built for version control, testing and documentation of SQL models.

5. Managed connectors handled extract and load. Off-the-shelf connectors now copy data from hundreds of common apps into warehouses with little setup, so teams can focus on the transform step.

AWS now calls ELT the standard choice for modern analytics.

Key takeaway: ELT won because cloud warehouses made compute elastic and storage cheap. If your target is a modern cloud warehouse, ELT is usually simpler and more flexible. Choose ETL deliberately, for a specific reason, not by default.

When ETL is still the better choice

ETL isn’t obsolete. Microsoft’s guidance recommends ETL when:

• The target system is constrained. If the destination can’t handle heavy processing, transform elsewhere first.
• Business rules are complex and need a specialized transformation engine.
• Compliance requires curated staging and audits before data is loaded.

Other common reasons:

• Sensitive data must never land raw. If you handle health records, payment card data or other regulated personal data, removing or masking fields before they reach the warehouse reduces exposure. See our guide to US state data privacy laws for the privacy rules that may apply.
• Edge and IoT data. AWS gives the example of filtering or averaging high-frequency sensor readings before sending them to the cloud, which cuts data volume and cost.
• Legacy sources with fixed formats that you transform once and load.

Many organizations run a hybrid: ETL for a few sensitive or awkward sources, ELT for everything else.

The trade-offs of ELT to manage

ELT’s flexibility comes with responsibilities:

• Raw data needs protection. Sensitive fields arrive untouched, so set up role-based access, column masking and clear ownership from day one. That’s core data governance.
• Warehouse costs can creep. Every transformation run uses warehouse compute. Schedule models sensibly, transform incrementally where possible and set budget alerts.
• Raw zones can get messy. Without naming conventions and documentation, raw tables pile up. Keep clear layers, for example raw, staging and final models.
• Data quality still matters. Loading first doesn’t mean skipping cleaning. Build tests for duplicates, nulls and broken values into your models. Our guide on how to clean data covers the most common fixes.

Related patterns: reverse ETL and streaming

Two more terms come up in the same conversations:

• Reverse ETL moves modeled data from the warehouse back into operational tools such as your CRM, marketing automation or support desk, so teams can act on it. Microsoft describes it as pushing curated data back to where business users can act.
• Streaming processes data continuously as it arrives instead of in scheduled batches. It suits fraud detection, equipment monitoring and real-time dashboards, but adds complexity most reporting doesn’t need.

Where to start: choosing for your business

Answer these 5 questions:

1. Where is the data going? A cloud warehouse or lakehouse points to ELT. A constrained or legacy target points to ETL.
2. Is there regulated data? If certain fields must never land raw, use ETL for those sources or mask them during extraction.
3. Who will maintain transformations? If analysts who know SQL will own them, ELT fits. If a dedicated engineering team runs a specialized tool, either works.
4. How often do questions change? Frequent new questions favor ELT, because raw data is already there to model.
5. How much data, and how fast? Large volumes favor ELT’s in-warehouse processing. Continuous real-time needs point to streaming.

A practical first setup for most small and midsize companies:

1. Choose a cloud data warehouse.
2. Use managed connectors to extract and load your top 3 sources into raw tables.
3. Write SQL models for 3 to 5 core tables, such as customers, orders and marketing spend.
4. Add tests for duplicates and missing keys.
5. Restrict access to raw tables and expose only modeled tables to BI tools.
6. Review warehouse spend monthly.

If you’re still deciding where the data should live, our comparison of data lake vs data warehouse helps.

Conclusion

ETL and ELT do the same job in a different order. ETL transforms data before loading, which suits constrained targets, strict pre-load compliance and legacy systems. ELT loads raw data and transforms it in the warehouse, which suits cloud platforms with elastic compute and teams that want flexibility. For most new cloud analytics projects, ELT is the sensible default, with ETL used deliberately for the sources that need it.

Sources

• Microsoft Learn: Extract, transform, load (ETL) — definitions of ETL and ELT, when to choose each, reverse ETL and streaming
• AWS: What’s the difference between ETL and ELT? — history of ETL, comparison of speed, cost, data types and security, and use cases
• dbt documentation: What is dbt? — transforming data inside the warehouse with SQL models, tests and documentation