Wiki

ETL vs ELT

A podcast-grounded comparison of transform-before-load and load-before-transform pipeline choices in modern data platforms.

Related Wiki Pages

ETL ELT Modern Data Stack Data Engineering Data Engineering Platforms Analytics Engineering dbt Reverse ETL

Definition and Scope

ETL means extract, transform, load. The pipeline applies business logic before the destination system receives the data. ELT means extract, load, transform. The pipeline loads raw or lightly processed data first, then transforms it in the warehouse or lakehouse.

Natalie Kwong defines the split in ETL vs ELT and the Modern Data Stack at 3:46-7:57. She uses source-specific extraction, business logic, and destination-specific loading to explain older ETL workflows.

The podcast archive treats ETL vs ELT as a pipeline boundary decision, not a tool ranking. The same source data can feed data engineering platforms, analytics engineering, data warehouses, and downstream reverse ETL workflows. The useful question is where transformation belongs for the consumer, risk, and ownership model in front of the team.

This comparison sits between ETL and ELT as standalone topic nodes, then use Modern Data Stack for the broader warehouse-centered stack. Use Data Warehouse vs Data Lakehouse for the storage-platform tradeoff.

For the shorter decision guide, use ETL vs ELT in the comparison section.

Link Map

Use these pages to place ETL vs ELT inside the archive.

Modern Data Stack explains ingestion and warehouse-side transformation, plus orchestration, observability, and activation around ELT workflows.
Data Pipelines covers the end-to-end movement from ingestion through processing and delivery.
Analytics Engineering and dbt cover the warehouse-side modeling work that made ELT practical for analysts and analytics engineers.
DataOps and GitOps for Data Teams cover the code, tests, lineage, and reproducibility practices that keep either pipeline maintainable.
Data Warehouse, Data Lake, and Data Warehouse vs Data Lakehouse cover where loaded data is written before or after transformation.
Data Quality and Observability covers the checks needed when raw, staged, modeled, and activated data can all fail in different ways.
Reverse ETL, Data Activation, and Customer Data Platforms cover what happens after modeled warehouse data is sent back to operational tools.

These podcast discussions anchor the comparison.

ETL vs ELT and the Modern Data Stack with Natalie Kwong is the main comparison episode. It defines ETL at 3:46 and uses customer acquisition cost as a transform-before-load example at 6:37. It explains ELT flexibility at 7:57, connects ELT to dbt and analyst autonomy at 12:39, and returns to ETL’s continued relevance at 41:30.
Analytics Engineer Skills and Tools with Victoria Perez Mola explains dbt as SQL transformations, version control, tests, and DAGs at 6:49-10:04.
Foundations of the Analytics Engineer Role with Juan Manuel Perafan frames analytics engineering as turning business reality into clean data at 11:03 and bringing software engineering rigor to workflows at 46:34.
Modern Data Pipeline Architecture with Santona Tuli distinguishes ingestion-focused pipeline authoring from dbt-style modeling at 10:48. It then covers staging, preprocessing, modeling, and marts at 32:57-43:05.
DataOps 101 for Scaling Data Platforms with Lars Albertsson adds the reproducibility focus. Mutable ETL results can change between runs unless the platform keeps immutable inputs and code-defined transformations (20:12-21:29 and 1:04:18).
Data-Led Growth, Event Tracking, and Reverse ETL with Arpit Choudhury shows event collection and storage. It also shows warehouse transformation, BI, and reverse ETL for activation at 22:50-41:30.

Common Definition

The archive’s shared definition is simple. ETL puts the transform before the destination, while ELT loads first and makes the warehouse or lakehouse the place where business-facing models are built. Kwong’s Airbyte example starts with the extract-load layer. It then places dbt-style transformation after data reaches the warehouse (ETL vs ELT and the Modern Data Stack, 3:19 and 31:31).

The common decision rule is to load first when future modeling flexibility is more valuable than pre-load control. Transform first when pre-load control is the main risk reducer. Kwong’s ELT section says loading source detail first helps when a new source field arrives or business logic changes. The team can then write a new warehouse transform instead of re-extracting data (ETL vs ELT and the Modern Data Stack, 7:57-10:00). Her ETL section still preserves the older approach for cases where the business logic must prepare the payload before it reaches the destination (3:46-6:37).

The comparison also has an ownership rule. ETL often keeps more of the work in data engineering or platform-owned jobs. ELT moves repeatable analytical logic toward SQL models owned by analytics engineers, analysts, or mixed data teams. Perez Mola’s analytics engineering episode ties that ELT-side work to dbt and tests. She also places it near DAGs, Snowflake, and Looker (Analytics Engineer Skills and Tools, 4:05-10:04).

Perafan later describes the same discipline as converting messy business reality into safer data systems (Foundations of the Analytics Engineer Role, 11:03-16:25).

Guest Differences

Natalie Kwong gives the warehouse-centered ELT case. Her argument favors preserving source detail and moving transformations closer to analysts. It also favors dbt-style SQL workflows after ingestion (ETL vs ELT and the Modern Data Stack, 7:57-12:39).

She doesn’t treat raw loading as a free-for-all. At 17:55-21:22 she separates raw ingestion from governed marts and warns about data swamps.

Lars Albertsson shifts the discussion from order of operations to reproducibility. His DataOps episode criticizes mutable warehouse-style ETL because results can differ when the same process runs at different times. His preferred design uses immutable inputs and functional transformations. Active datasets are defined in code (DataOps 101 for Scaling Data Platforms, 20:12-21:29 and 1:04:18). That view can support ELT-like raw retention, but the important line for him is immutability and lineage rather than the acronym.

Victoria Perez Mola and Juan Manuel Perafan focus on the role boundary created by ELT. Perez Mola treats dbt and Snowflake as daily analytics engineering tools. Looker, tests, and DAGs sit in the same workflow (Analytics Engineer Skills and Tools, 6:49-14:34). Perafan is less tool-specific, and his focus is modeling business reality safely with software engineering rigor (Foundations of the Analytics Engineer Role, 11:03 and 46:34).

Santona Tuli adds a pipeline authoring view. Her episode contrasts Upsolver and dbt by where they sit. Upsolver appears near ingestion and execution-engine concerns, while dbt appears near transformation and analytics engineering (Modern Data Pipeline Architecture, 10:48 and 24:57). She also names deduplication, ordering guarantees, and PII masking as pre-load concerns at 37:10. Those are reasons the transform step may not wait until the warehouse.

Arpit Choudhury starts from growth operations rather than pipeline theory. His data-led growth stack needs event collection and warehouse storage. It also needs dbt/BI analysis plus activation through operational tools (Data-Led Growth, Event Tracking, and Reverse ETL, 22:50-41:30). In that framing, ELT is useful only if the modeled warehouse layer is trusted enough to drive support and sales. It also has to support engagement and customer messaging.

Practical Comparison

Compare the two choices by where meaning is created and who must operate it.

In ETL, business logic runs before or during the load into the destination. Kwong’s customer acquisition cost example joins CRM and ad-spend data before consumption (ETL vs ELT and the Modern Data Stack, 6:37). In ELT, business logic usually runs after loading in a warehouse or lakehouse. Kwong connects that work to type casting, joins, SQL modeling, and dbt at 10:00-12:39.

ETL destinations receive curated payloads, modeled outputs, aggregates, or destination-specific records. That fits systems that shouldn’t receive broad raw history. ELT destinations receive raw or lightly processed source data first. Staging and modeled layers come later. Kwong separates raw ingestion from marts and consumer layers at 15:30-18:47 in the same episode.

ETL ownership often sits with data engineering or platform teams when the transform is part of ingestion, compliance, or operational delivery. ELT ownership often moves toward analytics engineers or analysts when the transform is SQL modeling, metrics, marts, and BI-ready data. Perez Mola describes that analytics engineering workflow at 4:05-10:04 in Analytics Engineer Skills and Tools.

ETL’s advantage is control before storage or delivery. It can reduce volume, protect fields, normalize records, and meet destination constraints. Tuli’s ingestion section names deduplication, ordering guarantees, and PII masking as pre-load concerns (Modern Data Pipeline Architecture, 37:10).

ELT’s advantage is flexibility after storage. It preserves source detail, supports remodeling without re-extraction, and lets SQL users iterate on business definitions. Kwong makes that case at 7:57-12:39.

The failure modes differ because ETL can hide transformation logic in jobs that are hard to reproduce when inputs change between runs. Albertsson’s DataOps discussion makes that risk explicit at 20:12-21:29 in DataOps 101. ELT can create untrusted raw zones if ownership and tests don’t follow. Documentation, cleanup, and governance have to follow too. Kwong warns about data swamps at 21:22 and unused data ownership at 43:02.

ETL Fit

Use ETL when the destination should only receive data that has already been filtered or prepared. It also fits data that must be masked or joined first. Kwong’s ETL definition includes source-specific extraction and organization-specific business logic before destination-specific loading (ETL vs ELT and the Modern Data Stack, 3:46-6:37). That makes ETL a good fit when the target is an operational system or a constrained data mart. It also fits legacy reporting workflows that expect curated tables rather than broad raw source copies.

ETL also fits when preprocessing has pipeline-level semantics. Tuli’s discussion of ingestion preprocessing includes deduplication, ordering guarantees, and PII masking (Modern Data Pipeline Architecture, 37:10). Those aren’t merely dashboard choices. They affect whether downstream storage is allowed to contain sensitive fields, duplicates, or incorrectly ordered events. In those cases, a transform-before-load step reduces risk before more consumers see the data.

ETL remains relevant in large enterprises and complex staging environments. Kwong says that older ETL workflows continue to appear when companies have established workflows. Heavy staging needs also keep ETL relevant (ETL vs ELT and the Modern Data Stack, 41:30).

Albertsson’s warning adds a condition. If those ETL jobs mutate shared tables or depend on changing source state, the team needs reproducibility. It also needs versioning and lineage controls (DataOps 101, 20:12-21:29 and 1:04:18).

ELT Fit

Use ELT when changing analytical logic is the bottleneck. Source-detail preservation lets teams write new transformations later when business logic or source fields change (ETL vs ELT and the Modern Data Stack, 7:57-10:00). This matches analytics engineering work because the transform becomes a maintained model rather than a one-off pipeline script.

ELT fits warehouse-centered analytics stacks. Kwong places Airbyte-style loading and dbt in the same modern stack. Warehouses and orchestration sit in that stack too, along with reverse flows (ETL vs ELT and the Modern Data Stack, 31:31-35:42).

Choudhury’s growth-stack episode shows the business version. Event collection and storage come before warehouse dbt models support BI analysis. The same warehouse layer supports segments and activation (Data-Led Growth Stack, 22:50-37:25).

ELT isn’t complete when raw data arrives. Perez Mola’s dbt workflow adds version control, tests, and a DAG to SQL transformations (Analytics Engineer Skills and Tools, 6:49-10:04). Perafan’s episode adds the reason those practices matter. The work turns messy business reality into a safer data system. Later sections call for software engineering rigor around data workflows (Foundations of the Analytics Engineer Role, 11:03 and 46:34).

Ownership and Governance

The ETL vs ELT decision changes who can safely change business logic. In an ETL flow, the transform may be tied to connector code, orchestration, or platform jobs. In an ELT flow, the transform often becomes a SQL model in the warehouse.

Kwong connects that shift to analyst autonomy and dbt at 12:39. Perez Mola connects the analytics engineer role to modeling and pipelines. She also connects it to quality and Looker at 4:05 (modern stack episode, analytics engineering episode).

The archive doesn’t support “load everything and sort it out later” as a good ELT rule. Kwong separates raw ingestion layers from marts and consumer-facing tables at 15:30-18:47. She also warns about governance and data swamp risk at 21:22, then returns to ownership and cleanup at 43:02.

That connects ELT to data governance and data observability. It also connects ELT to data quality, not only faster modeling.

Albertsson’s DataOps view adds a stricter operating rule. Keep active outputs defined in code and make transformation history traceable (DataOps 101, 1:04:18). Whether the team says ETL or ELT, unclear lineage creates the same failure mode. Consumers can’t tell which transformation created a dataset. They also can’t tell why it changed or whether a rerun should reproduce the same result.

Downstream Activation

The comparison matters more when transformed data leaves analytics and changes customer-facing work. Choudhury’s data-led growth stack moves from collection and storage to BI. It then moves to reverse ETL and operational analytics tools such as Census, Hightouch, and Grouparoo (Data-Led Growth Stack, 28:52-37:25). At that point, a metric or segment isn’t only a dashboard definition. It may drive support context, sales routing, engagement campaigns, or onboarding.

Kwong also includes reverse data flows in the modern stack at 35:42 (ETL vs ELT and the Modern Data Stack). That makes ELT quality visible outside the warehouse. A warehouse transform that is good enough for exploration may not be good enough for data activation.

Before a team syncs modeled data into operational tools, the modeled layer needs tests and ownership. It also needs documentation and a clear refresh path.

Use these pages for the adjacent platform, modeling, and activation topics.