ETL vs ELT in AWS: Differences, Use Cases, and How to Choose
ETL transforms data before it enters the warehouse. ELT loads raw data first and transforms it inside the warehouse using SQL. Both patterns are widely used on AWS, and many production platforms use both together.
Quick decision rule: choose ETL when transformations are complex, compliance-sensitive, or better handled outside the warehouse. Choose ELT when transformations are SQL-friendly and warehouse-native. Use both when you need upstream reshaping (Glue or EMR) alongside downstream business logic (Redshift and dbt).
Simple explanation
Both patterns move data from source systems to analytics tables, but they disagree on one thing: where the cooking happens.
Kitchen analogy
Think of a data pipeline as a kitchen. Source systems produce raw ingredients — messy and inconsistent. Analytics tables are the finished dishes users eat from. ETL means you cook in a separate kitchen (a Spark cluster) and only bring finished food into the dining room (the warehouse). ELT means you carry raw ingredients straight into the dining room and cook there, using the warehouse’s own stove (SQL).
Some terms used throughout this page:
- Raw data: data as it arrives from the source, unmodified. May be JSON, CSV, or database exports.
- Staging: an intermediate schema where raw data lands before transformation.
- Warehouse: a columnar database optimized for analytics, like Amazon Redshift.
- Marts / curated tables: clean, business-ready tables built from staging data for BI tools and analysts.
How ETL works in AWS
In ETL, a processing engine sits between the source and the warehouse. It extracts raw data, applies transformations, and loads only the clean result into the destination.
A typical AWS ETL flow:
Source (S3, RDS, Kafka)
→ Extract raw data
→ AWS Glue or EMR Spark job (clean, filter, reshape, join)
→ Load transformed output into Redshift staging or S3 processed zoneFor example: a Glue job reads deeply nested JSON from an S3 raw zone, flattens the structure, filters invalid records, casts data types, and writes Parquet to a processed S3 prefix. Redshift then loads it via COPY. The raw JSON never enters Redshift at all.
Primary AWS ETL tools:
- AWS Glue: serverless Spark-based ETL. Write PySpark or Python Shell jobs; Glue manages the cluster.
- Amazon EMR: managed Spark or Hive clusters for large-scale or custom transformation workloads.
- AWS Lambda: lightweight event-driven transformations on small, simple payloads.
How ELT works in AWS
In ELT, raw data loads into the warehouse first — into a staging or raw schema — and SQL transformations then build the clean analytics tables from there.
A typical AWS ELT flow:
Source (S3, databases, APIs)
→ Load raw data into Redshift staging schema (via COPY or ingestion tool)
→ Transform with SQL (dbt models or Redshift stored procedures)
→ Clean tables in marts schema, ready for BI toolsFor example: an S3 file lands in raw.orders via COPY. A dbt model reads raw.orders, applies cleaning logic in SQL, and materializes staging.orders_cleaned. A second model aggregates that into marts.daily_order_summary. See loading data into Redshift for ingestion patterns.
Primary AWS ELT tools:
- Amazon Redshift: the warehouse where raw data lands and SQL transformations run.
- dbt on Redshift: organizes SQL transformations as version-controlled models with dependency graphs, testing, and documentation.
- Redshift stored procedures: SQL-based transformation logic for simpler use cases without a full dbt setup.
If your source data arrives as flat rows (relational database exports, CSV files, structured API payloads) and your team already knows SQL, ELT on Redshift is almost always the faster path. You skip the Spark cluster entirely and let Redshift do the work it was built for.
ETL vs ELT: side-by-side comparison
| Property | ETL | ELT |
|---|---|---|
| Order of operations | Extract → Transform → Load | Extract → Load → Transform |
| Where transformations run | External engine (Glue, Spark, EMR) | Inside the warehouse (Redshift SQL) |
| Raw data in warehouse? | No — only cleaned data enters | Yes — raw data loads into staging first |
| Who usually owns the logic | Data engineers (Python, Scala) | Analysts or engineers (SQL) |
| Best fit data shape | Nested JSON, semi-structured, binary | Structured or lightly structured data |
| Speed of iteration | Slower — redeploy Glue/EMR jobs | Faster — edit a SQL model and run dbt |
| Cost model | Glue/EMR compute + warehouse storage | Warehouse compute + warehouse storage |
| Governance and compliance fit | Strong — raw data can stay outside the warehouse | Requires careful schema separation inside |
| Warehouse dependency | Lower — can output to S3 or multiple targets | Higher — warehouse is the transformation engine |
| Typical AWS tools | Glue, EMR, Lambda | Redshift, dbt, Redshift stored procedures |
When to use ETL
ETL is the stronger choice when your data is too messy or too complex to transform inside a SQL warehouse.
- Deeply nested or semi-structured data. Source JSON with arrays-of-arrays or complex schemas is difficult to handle in SQL. Apache Spark on Glue or EMR handles this natively.
- Heavy Python or Spark logic. ML feature engineering, statistical preprocessing, or custom parsing that has no clean SQL equivalent belongs in a Spark job.
- Compliance rules that prevent raw data from entering the warehouse. When data contains PII that must be masked or dropped before storage, ETL gives you a controlled transformation layer outside the warehouse.
- Large preprocessing before Redshift ingestion. Running EMR Spark on hundreds of GB of raw files before COPY into Redshift can reduce both warehouse compute cost and ingest time.
- Data lake-centric workloads. Architectures where S3 is the primary store benefit from ETL pipelines that write clean Parquet files to S3 output zones. See data lake architectures for how zone design fits.
Many teams default to ETL and Spark because it feels more “proper” engineering. If your transformation can be expressed in a SQL SELECT and runs in minutes on Redshift, you do not need a Spark cluster. Unnecessary ETL adds deployment overhead, a second system to monitor, and a new skill requirement on your team.
When to use ELT
ELT is the stronger choice when your data is already structured and your team lives in SQL.
- Transformations can be expressed in SQL. Filtering, joining, aggregating, and renaming columns are native SQL operations. If your logic fits in a SELECT statement, you do not need Spark.
- Analysts own transformation logic. dbt empowers SQL-fluent analysts to write, test, and version transformations without requiring data engineering support on every change.
- Fast model iteration. Changing business logic in a dbt SQL model takes minutes. Updating a Glue PySpark job, testing it, and redeploying takes longer.
- BI and reporting transformations. Final-layer aggregations such as daily summaries, customer segments, and KPI tables are a natural fit for SQL running inside Redshift.
- Structured data already landing in the warehouse. If you are loading from relational databases or flat CSV files, there may be no need for a pre-warehouse transformation step. Load raw, transform in place.
Why many AWS teams use both
In practice, most mature AWS data platforms use ETL and ELT together, with each handling the layer it suits best.
A common hybrid pattern:
- ETL upstream: Glue or EMR reads raw event data from S3, flattens nested JSON, drops invalid records, and writes clean output to a Redshift staging schema.
- ELT downstream: dbt reads from the Redshift staging schema and runs SQL models to produce curated marts tables for BI tools and dashboards.
The boundary between the two layers often sits between the data lake and the data warehouse. Glue handles the lake side; dbt handles the warehouse side. This maps directly to the raw → staging → marts zone model described in data pipeline design.
Splitting responsibilities this way also clarifies ownership: data engineers own Glue/EMR pipelines; analytics engineers or analysts own dbt models. Neither team blocks the other on day-to-day changes.
Using both does not mean duplicating logic. Define a clear handoff point — typically the boundary between staging and marts — and keep each side’s logic in its own layer. When the same transformation logic appears in both a Glue job and a dbt model, you end up with two sources of truth and no way to know which is right.
AWS Glue vs dbt on Redshift
These two tools are often compared directly, but they solve different problems at different layers.
| Dimension | AWS Glue | dbt on Redshift |
|---|---|---|
| Where it runs | Outside the warehouse (Spark cluster) | Inside the warehouse (Redshift SQL) |
| What it transforms | Files, streams, raw data from any source | Tables already inside Redshift |
| Language | PySpark, Python Shell, Scala | SQL (SELECT statements) |
| Who uses it | Data engineers | Analytics engineers, SQL-fluent analysts |
| Pattern it supports | ETL | ELT |
| When you need it | Complex pre-warehouse transformations | In-warehouse business logic and mart builds |
AWS Glue moves and reshapes data before it reaches Redshift. dbt organizes SQL transformations for data that is already in Redshift. Most teams that use dbt also use Glue or another ingestion tool upstream. Choosing one does not replace the other.
Common beginner mistakes
Using Spark when SQL is enough. Defaulting to Glue or EMR for every transformation adds operational overhead without benefit. If your logic fits in a SQL SELECT, use ELT.
Loading raw data directly into production schemas. In ELT, raw data should land in a staging schema (
raworstaging), not directly into production tables. Transformation layers should read from staging and write to marts. Skipping staging makes reprocessing and debugging painful.Splitting the same transformation logic across ETL and ELT layers. If Glue handles half the business logic and dbt handles the other half, it becomes unclear which system is authoritative. Define clean boundaries: Glue owns raw-to-staging; dbt owns staging-to-marts.
Treating ELT as “no governance.” Loading raw data into the warehouse does not mean anything goes. Raw schemas still need access controls, data quality checks, and documented ownership. dbt tests cover the minimum.
Choosing tools before deciding where transformations should live. The right question is not “Glue or dbt?” It is “should this transformation happen inside or outside the warehouse?” Answer that first, then pick the tool.
Summary
- ETL transforms before loading: a processing engine (Glue, Spark, EMR) cleans data before it enters the warehouse
- ELT loads first then transforms: raw data lands in a warehouse staging schema; SQL builds clean tables from there
- ETL fits complex, nested, or compliance-sensitive data; ELT fits SQL-friendly, fast-iteration workloads
- Most mature AWS platforms use both: Glue for upstream reshaping, dbt for downstream warehouse modeling in Redshift
- AWS Glue and dbt operate at different layers of the same pipeline and are complementary, not competing
Frequently asked questions
What is the difference between ETL and ELT?
ETL (Extract, Transform, Load) transforms data before loading it into the destination. A processing engine like Glue or Spark cleans and reshapes the data, then the result lands in the warehouse. ELT (Extract, Load, Transform) loads raw data into the warehouse first, then transforms it inside using SQL. The key difference is where and when transformations run.
When should I use AWS Glue instead of Redshift SQL or dbt?
Use AWS Glue when your source data is deeply nested JSON or semi-structured, when transformations need Spark-level compute, or when compliance rules say raw data cannot enter the warehouse. Use Redshift SQL or dbt when transformations can be expressed in SQL, your data is already in Redshift, and you want fast iteration from analysts or engineers.
Can ETL and ELT be used together in one AWS architecture?
Yes, most mature AWS data platforms use both. A common pattern is ETL upstream (Glue or EMR processes raw data from S3 into a staging schema in Redshift) and ELT downstream (dbt runs SQL transformations from staging into analytics or marts tables). Each layer handles what it does best.
Is dbt an ETL tool or an ELT tool?
dbt is an ELT tool. It runs SQL SELECT statements inside your warehouse — it does not move or extract data. dbt handles the T (transform) step of ELT. You still need a separate process (COPY command, Fivetran, or Glue) to handle the E (extract) and L (load) steps.
Does ELT mean raw data should go straight into production tables?
No. Even in ELT, raw data should land in a staging schema (raw or staging), not in production tables. Transformation layers — usually dbt models — read from staging and write to curated schemas like marts or analytics. Skipping staging makes it hard to reprocess or debug data later.