The debate between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) has been running in data engineering circles for over a decade. In 2025, it's still unsettled — but for different reasons than it was five years ago. The question is no longer primarily about technical capability. Modern cloud data warehouses have made ELT more accessible than ever. The question is now about organizational fit, data governance requirements, and where your team's skills actually live.
This is a practical decision guide, not a theoretical overview. I'll explain both approaches, when each wins, and the specific signals in your organization that should drive the choice.
What ETL Actually Means (Beyond the Acronym)
In ETL, data is extracted from source systems, transformed outside the destination — in a dedicated integration layer, an ETL tool, or custom code — and then the clean, transformed data is loaded into the data warehouse or data mart.
The transformation happens before the data touches the warehouse. The warehouse sees clean, structured, ready-to-use data. The integration layer holds all the business logic: deduplication rules, data type conversions, joins, aggregations, and domain-specific transformations.
Typical ETL stack in 2024–25:
- Extraction: Azure Data Factory, Fivetran, custom Python scripts
- Transformation: Azure Data Factory dataflows, Python (Pandas, PySpark), SSIS
- Load target: Azure Synapse Analytics, SQL Server, on-premises data warehouse
- Orchestration: Apache Airflow, Azure Data Factory pipelines
You have strict data governance requirements, are working with sensitive regulated data (financial, healthcare), have a large legacy system landscape, or are loading into an on-premises data warehouse where storage and compute are expensive.
What ELT Actually Means
In ELT, data is extracted from source systems and loaded into the destination — a cloud data warehouse — in raw or near-raw form first. Transformations happen inside the warehouse, using SQL, after the data has landed.
The data warehouse becomes both the storage layer and the transformation engine. The warehouse's own compute power does the heavy lifting. This is possible in 2025 in a way it wasn't in 2015 because modern cloud warehouses (Snowflake, BigQuery, Databricks, Azure Synapse) have virtually unlimited and independently scalable compute — running complex SQL transformations at massive scale is cheap and fast.
Typical ELT stack in 2024–25:
- Extraction & load: Fivetran, Airbyte, custom connectors
- Transformation (in-warehouse): dbt (data build tool) with SQL models
- Load target: Snowflake, BigQuery, Databricks, Azure Synapse
- Orchestration: dbt Cloud, Airflow, Prefect
You're using a cloud-native data warehouse, your team is more comfortable with SQL than Python, you want to iterate quickly on transformation logic, and you need to preserve raw data for reprocessing or audit purposes.
The Real Trade-offs: A Direct Comparison
Marketing for tools on both sides of this debate tends to overstate the advantages of each approach. Here's an honest comparison of the dimensions that actually matter in enterprise deployments:
Data Governance and Compliance
ETL wins here, particularly in regulated industries. When PII, financial records, or healthcare data needs to be masked, anonymised, or filtered before storage, ETL gives you control at the point of ingestion. With ELT, sensitive data lands in the warehouse raw — even if it's transformed immediately, it exists in an unmasked state, which can create compliance gaps in HIPAA, GDPR, or RBI-regulated environments.
Verdict: ETL for regulated data; ELT is workable with careful warehouse-level access controls for less sensitive data.
Development Speed and Agility
ELT wins decisively. dbt models are SQL files in a version-controlled repository. A data analyst who knows SQL can write, test, and deploy a new transformation in an afternoon without touching the ETL pipeline. In an ETL world, adding a new transformation often means modifying a pipeline definition, redeploying an integration service, and hoping nothing breaks downstream. The iteration velocity difference is significant — ELT teams consistently ship analytics features faster.
Verdict: ELT for analytics agility; ETL is appropriate when pipeline stability and formal change control matter more than speed.
Historical Data and Reprocessing
ELT wins because raw data is always preserved in the warehouse. If your transformation logic was wrong six months ago, you can fix the dbt model and reprocess historical data without re-extracting from source systems. In an ETL world, if the transformation was wrong, the raw data is gone — only the transformed output was loaded. Reprocessing requires going back to source, which may no longer be possible.
Verdict: ELT for data lineage and reprocessability.
Cost at Scale
This is nuanced. ELT with cloud compute can become expensive at scale if transformations are run inefficiently — a poorly written dbt model that scans a 500-million-row table on every run will generate a meaningful Snowflake bill. ETL with optimized transformation logic running outside the warehouse can be cheaper at very large scales because you're not paying cloud warehouse compute for every transformation step. For most mid-market organizations (under 100M rows of core data), the difference is negligible. For data-intensive enterprises, it becomes a real consideration.
Verdict: Context-dependent; model the costs for your expected data volumes before committing.
Tooling and Skills Availability
This is where many real-world decisions actually get made. In India and Southeast Asia particularly, there's a deep bench of Python and SQL developers who can work in either paradigm — but dbt expertise and modern ELT tool knowledge (Fivetran, Airbyte) is more concentrated in cloud-native organizations. If you're building a new team or have existing ETL expertise (SSIS, ADF, Informatica), the switching costs matter.
Verdict: Use what your team can actually build, maintain, and debug when something breaks at 2am.
The Hybrid Approach Most Mature Organisations Use
The framing of "ETL vs ELT" implies a binary choice. In practice, most well-architected data platforms in 2025 use both — assigning each approach to the layer where it makes the most sense:
- Ingestion layer (ETL pattern): PII masking, format normalisation, sensitive data filtering happen before data lands in the warehouse
- Storage layer (ELT pattern): Raw data preserved in a staging zone; dbt handles downstream modelling into analytics-ready tables
- Serving layer: Pre-aggregated, business-logic-rich tables that Power BI or your BI tool queries directly
This hybrid architecture captures the governance advantages of ETL at the boundary (where data enters your platform) and the agility advantages of ELT in the interior (where analysts iterate on business logic).
The question isn't "ETL or ELT?" — it's "where in my pipeline does transformation need to happen, and what are the requirements at each point?" That framing leads to much better architecture decisions than treating it as an either/or.
The Decision Framework: Questions to Ask Your Team
Before committing to an architecture, answer these questions honestly:
- Regulatory: Do any of your source data sets contain PII, PHI, or data subject to GDPR, HIPAA, or RBI regulations? → Requires ETL-style transformation at the ingestion boundary
- Warehouse: Are you on a modern cloud warehouse (Snowflake, BigQuery, Synapse, Databricks)? → ELT is viable; on-premises or legacy warehouse favours ETL
- Team skills: Is your team more comfortable in Python/Spark or in SQL/dbt? → Match the architecture to the skill set that can maintain it
- Iteration speed: How often does your transformation logic change? → High iteration frequency favours ELT/dbt; stable transformations can live in either
- Data volumes: Are you working with billions of rows of high-frequency event data? → Model the cost of cloud compute for ELT transformations before committing
- Existing investment: Do you have significant ETL tooling or expertise already? → The cost of switching must be weighed against the benefits
If you're building a new data platform and don't have overriding compliance constraints: start with ELT using a cloud warehouse + dbt + Fivetran or Airbyte. The tooling is mature, the community is large, and the analytics iteration speed advantage is significant. Implement ETL-style controls only at the data boundary for regulated data. You can always add ETL complexity later — it's much harder to add ELT agility to an entrenched ETL architecture.
A Practical Note on dbt in 2025
No article on ELT in 2025 would be complete without addressing dbt specifically. dbt (data build tool) has become the de facto standard for in-warehouse transformation, and for good reason: it brings software engineering practices (version control, testing, documentation, modularity) to SQL transformations that previously lived in undocumented stored procedures or ad-hoc scripts.
The adoption of dbt doesn't mean you don't need data engineers — it means data engineers spend their time on data modeling and business logic rather than writing plumbing code. It also lowers the barrier for analysts to contribute to the transformation layer, which is one of the most significant productivity multipliers available to analytics teams today.
If you're evaluating ELT and haven't looked at dbt Cloud or dbt Core, it should be the first tool you evaluate.
Phoenix Solutions designs and builds data pipeline architectures using both ETL and ELT approaches — selecting the right tool for each layer of your platform based on your governance requirements, team skills, and existing infrastructure. Book a free 30-minute architecture review and we'll give you an honest assessment of what your organization actually needs.