ETL: Extract, Transform, Load
ETL (Extract, Transform, Load) is the historical paradigm for data pipelines. Emerging in the 1970s-1980s alongside the first on-premise data warehouses (Teradata, Oracle), it follows a simple logic: extract data from sources, transform it in an intermediate staging area, then load the clean result into the destination warehouse.
Transformation is at the heart of the process: cleansing, aggregation, joins, applying business rules, format conversion. Everything happens before the data lands in the Data Warehouse. The warehouse only receives clean, structured, ready-to-use data.
Traditional ETL architecture and tools
In a classic ETL architecture, a dedicated transformation server (often called an ETL server or staging area) handles all the work. Legacy tools like Informatica PowerCenter, IBM DataStage, Talend, or Microsoft SSIS encapsulate this logic in graphical interfaces and pre-built connectors.
This approach requires upfront design effort: every transformation must be specified before data enters production. Readability is a genuine advantage - you know exactly what enters the warehouse - but rigidity becomes a real obstacle when analytical needs evolve rapidly.
ETL was designed when storage was expensive and servers were limited in power. Loading raw, untransformed data would have been wasteful - hence the systematic upstream transformation.
ELT: Extract, Load, Transform
ELT (Extract, Load, Transform) reverses the order: extract data, load it directly into the warehouse or data lake (raw, untransformed), then transform it in-place using SQL or frameworks like dbt. This approach was born with the cloud and modern data warehouses.
The fundamental shift is that the data warehouse itself becomes the transformation engine. BigQuery (Google), Snowflake, Amazon Redshift, and Databricks offer elastic compute power and near-free storage. Transforming terabytes of data in SQL directly inside Snowflake is today faster and cheaper than doing it on an intermediate ETL server.
The structural advantages of ELT
Preserving raw data is the most underrated advantage of ELT. By loading source data without transformation, you create an immutable historical layer. If business rules change in six months, you can replay transformations without re-extracting sources.
Flexibility is equally significant: Data Scientists and Analysts can access raw data for their own analyses without waiting for an ETL pipeline to be reconfigured. This fundamentally changes the dynamic between data teams.
According to Gartner, more than 65% of new data architectures in large enterprises now adopt an ELT-first approach, compared to less than 20% in 2018. The shift to the cloud is the primary driver of this transition.
Gartner, Magic Quadrant for Data Integration Tools, 2023The 5 fundamental differences between ETL and ELT
Beyond the order of operations, ETL and ELT differ across five dimensions that directly impact your architecture choices, tooling decisions, and team organization.
1. Location of transformation
In an ETL pipeline, transformation occurs in a system external to the data warehouse - a dedicated server, Spark cluster, or virtual machine. In an ELT pipeline, transformation happens directly inside the data warehouse or data lake, using SQL or an integrated compute engine.
2. Raw data preservation
ETL does not preserve raw data in the warehouse - only transformed data is stored. ELT first loads raw data into a raw layer, enabling you to replay transformations and answer analytical questions that were not anticipated at design time.
3. Speed to production
ETL requires specifying transformations before loading, which lengthens the development cycle. With ELT and tools like dbt, a Data Analyst can write a new transformation in SQL and deploy it in hours rather than days.
4. Cost structure
Traditional ETL solutions carry high licensing costs (Informatica, MicroStrategy) and require dedicated staging servers. ELT leverages the compute power of the cloud data warehouse - costs are directly tied to usage and decrease with columnar compression and object storage.
5. Privacy and security
ETL has an advantage here: sensitive data can be masked or pseudonymized before entering the warehouse. With ELT, raw data is loaded first in full - you need to ensure the raw layer is properly secured and that data masking policies are applied upstream of analytical views.
Key tools of the modern data pipeline
The ELT ecosystem has structured itself around four categories of tools covering the entire pipeline: ingestion, orchestration, transformation, and observability.
Ingestion: Fivetran, Airbyte, Stitch
These tools handle extraction and loading (EL without the T). Fivetran is the market leader with pre-built connectors for hundreds of sources (Salesforce, Hubspot, SQL databases, APIs). Airbyte is the open-source alternative with an active community. Both handle incremental replication, schema change detection, and connection errors automatically.
Transformation: dbt (data build tool)
dbt has become the de facto standard for the ELT transformation layer. It lets you write transformations in pure SQL, document them, test them, and version them in Git. dbt turns the data warehouse into an execution engine and produces automatic lineage for all transformations. The Cloud version (dbt Cloud) adds orchestration and team collaboration.
Orchestration: Apache Airflow, Prefect, Dagster
Orchestration coordinates pipeline execution in the right order at the right time. Apache Airflow, created by Airbnb, is the open-source standard with its DAGs (Directed Acyclic Graphs) in Python. Prefect and Dagster are more modern alternatives with better observability and native data dependency management.
Distributed processing: Apache Spark
For transformations on very large volumes (terabytes to petabytes), Spark remains the standard. It integrates into ELT architectures for pre-processing steps that exceed the native SQL capabilities of cloud warehouses. PySpark is the most widely used Python API. Spark on Databricks or EMR (AWS) is the most common production configuration.
In 2026, the most widespread modern data pipeline is: Fivetran (ingestion) + Snowflake or BigQuery (storage) + dbt Core (transformation) + Airflow or dbt Cloud (orchestration). This combination covers 80% of the analytical needs of data teams.
How to choose between ETL and ELT
The ETL vs ELT decision does not happen in the abstract - it depends on your existing infrastructure, data volume, regulatory requirements, and team skills.
When to prefer ETL
ETL remains relevant in several situations: on-premise infrastructure without a cloud data warehouse, strict regulatory requirements on personal data (masking must happen before loading), integration with legacy systems that impose a specific format, and teams accustomed to classical ETL tools without short-term migration capacity.
When to prefer ELT
ELT is the right choice for most greenfield projects in 2026: fully cloud stack, need for analytical flexibility, team proficient in SQL and dbt, significant volumes that benefit from the elastic compute power of the cloud warehouse, and desire to give Data Scientists direct access to raw data.
The hybrid ETL+ELT approach
In practice, most organizations combine both. An ETL pipeline may handle sensitive data (anonymization before loading), while the rest of the flow adopts ELT. Some modern tools like Spark also offer the flexibility to do both depending on the need.
Consolidating ETL and ELT with spaced repetition
Data engineering concepts are dense and specific. Knowing how to define ETL and ELT in an interview is one thing; understanding the architectural implications of each choice is another. Spaced repetition (FSRS algorithm) is ideal for anchoring these distinctions in long-term memory.
Memia's 'ETL/ELT Pipelines and Orchestration' deck covers definitions, tools, use cases, and common interview questions on the topic. Each card is formulated to test understanding rather than simple memorization.
For data engineering concepts, the best strategy is to create 'comparison' cards: ETL vs ELT, Airflow vs Prefect, dbt Core vs dbt Cloud. These comparisons are the most frequently asked questions in technical interviews.
Frequently asked questions about ETL and ELT
What is the main difference between ETL and ELT?
The difference is the timing and location of transformation. In ETL, data is transformed before being loaded into the warehouse (in an intermediate system). In ELT, raw data is loaded first, then transformed directly in the data warehouse using SQL or dbt.
Why has ELT replaced ETL in modern architectures?
ELT prevailed thanks to the cloud: data warehouses like BigQuery, Snowflake, and Redshift offer elastic compute power and cheap storage. Transforming in-place in the warehouse became faster and cheaper than maintaining an intermediate ETL server.
Is dbt an ETL or ELT tool?
dbt is a pure ELT tool: it handles only the Transform (T) layer and executes it directly in your data warehouse. It does not handle extraction or loading - those steps are managed by dedicated tools like Fivetran or Airbyte.
Can Airflow orchestrate both ETL and ELT pipelines?
Yes. Apache Airflow is a generic orchestrator that can execute any type of task: calls to traditional ETL tools, Spark jobs, SQL queries, dbt workflows. It coordinates the order and timing of executions without imposing a particular paradigm.
Is ETL more secure than ELT for sensitive data?
ETL has an advantage here: data can be masked or pseudonymized before entering the warehouse. With ELT, raw data is loaded in full - you need to implement masking in the raw layer policies and ensure that only transformed views are accessible to end users.
What is the 'staging area' in an ETL pipeline?
The staging area is a temporary intermediate space where data is temporarily stored between extraction and transformation. It allows buffering data, handling loading errors, and replaying transformations without re-extracting sources if needed.
Can you do ETL with Spark?
Yes, Spark is often used in ETL architectures for complex transformations on large volumes. Spark can read from multiple sources, transform data via DataFrames or Spark SQL, and write to a data warehouse or data lake. It is particularly suited to transformations that exceed native SQL capabilities.