Comparison
ETL vs ELT
A podcast-grounded decision guide for choosing transform-before-load or load-before-transform pipelines in modern data stacks.
Related Wiki Pages
ETL and ELT answer one pipeline decision: the team has to choose where transformation happens. In ETL, the team extracts data, transforms it, and loads the prepared result. In ELT, the team extracts data, loads it first, and transforms it later inside the warehouse or lakehouse.
The DataTalks.Club archive treats the choice as an ownership and risk decision, not a tool slogan. Natalie Kwong defines ETL and ELT in ETL vs ELT and the Modern Data Stack. She explains ETL at 3:46 and uses customer acquisition cost as a transform-before-load example at 6:37. She explains ELT’s flexibility at 7:57, then connects ELT to dbt and analyst autonomy at 12:39.
For the full archive-backed reference, use ETL vs ELT. Use ETL for transform-before-load pipelines and ELT for load-first warehouse modeling.
Short Comparison
Use ETL when the destination should receive curated data only. This fits operational systems and constrained marts. It also fits compliance-heavy targets where masking, deduplication, or joins must happen before broad storage.
Use ELT when future modeling flexibility matters more than pre-load control. This fits warehouse-centered analytics stacks where teams preserve source detail and write new SQL models later.
The difference changes who can safely change business logic:
- ETL often keeps transformation logic in data engineering, ingestion, or platform jobs.
- ELT often moves transformation logic into SQL models owned by analytics engineers, analysts, or mixed data teams.
- Both need DataOps practices because either path can fail without versioned code, tests, lineage, and ownership.
Lars Albertsson makes the reproducibility risk explicit in DataOps 101 for Scaling Data Platforms. At 20:12-21:29, he warns that mutable ETL results can differ across runs when inputs change. At 1:04:18, he ties active datasets to code and versioning, with lineage as the audit path.
ETL Fit
Choose ETL when the target shouldn’t receive broad raw data. Kwong’s customer acquisition cost example joins CRM data with ad-spend data. The reporting layer then consumes the prepared result (ETL vs ELT and the Modern Data Stack, 6:37). That’s a good fit when the target expects a prepared metric or mart rather than source-level detail.
ETL also fits when preprocessing reduces risk before storage. Santona Tuli describes ingestion-stage deduplication, ordering guarantees, and PII masking in Modern Data Pipeline Architecture (37:10). Those steps change what downstream tables can expose. They may belong before the data reaches a human-facing warehouse or lakehouse layer.
ETL remains useful in large enterprises and complex staging environments. Kwong says established enterprise workflows and heavy staging needs can keep ETL relevant (41:30). The right question is whether the transform protects the target or only hides useful source detail from future modeling work.
ELT Fit
Choose ELT when questions or source fields change often. Kwong says ELT keeps source detail available for later transformation work. Analysts and analytics engineers can add new models when the business question changes (ETL vs ELT and the Modern Data Stack, 7:57-12:39). That’s why ELT sits close to analytics engineering and dbt.
Victoria Perez Mola shows the operating side of ELT in Analytics Engineer Skills and Tools. At 4:05-10:04, she puts dbt and SQL models inside the analytics engineering workflow. The same operating context includes tests and DAGs alongside Snowflake and Looker. ELT works only when the team maintains the warehouse-side transform like production code.
ELT also fits activation and growth stacks. Arpit Choudhury describes a growth stack that starts with event collection and warehouse storage. It then adds BI and reverse ETL in his event-tracking episode (22:50-41:30). In that setup, the modeled warehouse layer has to be trusted because it may drive support context and sales routing. It may also drive customer messaging or onboarding.
Tool Boundaries
Don’t map ETL vs ELT directly to one vendor, because Kwong separates orchestration from loading and transformation. Airflow schedules jobs, while Airbyte handles extract-load work and dbt handles warehouse transformations after data arrives (ETL vs ELT and the Modern Data Stack, 30:59-33:45).
Tuli gives the same boundary from a pipeline-authoring view. At 10:48 in Modern Data Pipeline Architecture, she contrasts ingestion-focused pipeline authoring with dbt-style modeling. At 39:23-43:05, she moves from transformation and data modeling into marts and dashboards. Metrics sit in that same business-facing layer. That split is often the real ETL-versus-ELT boundary.
The orchestrator doesn’t decide the acronym. A pipeline can use Airflow, Prefect, Dagster, or another scheduler and still be ETL or ELT. The team still has to decide where business meaning becomes durable and who owns the change path.
Decision Checklist
Start from the target and the failure mode.
- Use ETL if the target must receive masked, joined, filtered, or ready-to-use data.
- Use ETL if pre-load validation protects compliance, data volume, ordering, or operational constraints.
- Use ELT if future business questions require raw source detail and flexible SQL modeling.
- Use ELT if analytics engineers or analysts own the transformation layer and can maintain tests, docs, and dependencies.
- Use either pipeline choice only with clear ownership, lineage, and quality checks.
The archive gives a practical rule: transform early when the target needs protection, and load first when the team needs future modeling flexibility. Don’t use ELT as a reason to ignore governance. Don’t use ETL as a reason to hide source detail that future teams will need.
Related Pages
These pages cover the deeper topic nodes behind the decision: