ETL vs ELT: Choosing the Right Data Pipeline for Your AWS Workload

Choosing between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) can significantly impact the performance, cost, and maintainability of your data pipeline. Both approaches have their place in modern data architectures, and AWS provides services that support each pattern. This guide compares these approaches and helps you choose the right one for your workload.

Understanding ETL and ELT

Before diving into AWS-specific implementations, let's clarify what distinguishes these two approaches.

ETL: Extract, Transform, Load

In ETL pipelines, data is extracted from source systems, transformed in a dedicated processing environment, and then loaded into the target destination. The transformation happens outside the target system, typically in a staging server or ETL tool.

┌─────────────┐     ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Source    │────▶│   Extract   │────▶│  Transform  │────▶│    Load     │
│   Systems   │     │   (Glue)    │     │  (Glue/EMR) │     │  (Target)   │
└─────────────┘     └─────────────┘     └─────────────┘     └─────────────┘

ELT: Extract, Load, Transform

In ELT pipelines, raw data is extracted and loaded directly into the target system. Transformations happen inside the target (typically a data warehouse) using its native compute power.

┌─────────────┐     ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Source    │────▶│   Extract   │────▶│    Load     │────▶│  Transform  │
│   Systems   │     │   (Glue)    │     │ (Redshift)  │     │  (SQL/DW)   │
└─────────────┘     └─────────────┘     └─────────────┘     └─────────────┘

Key AWS Services for Data Pipelines

AWS offers three primary services that data engineers commonly use for data pipelines. Each has distinct characteristics that make it suited for different stages and patterns.

AWS Glue

AWS Glue is a fully managed, serverless data integration service. According to AWS documentation, it consolidates major data integration capabilities including data discovery, modern ETL/ELT pipelines, data cleansing, and workflow orchestration into a single service.

Key capabilities: - Serverless architecture: No infrastructure to manage; automatic scaling based on workload - Data Catalog: Centralized metadata repository that integrates with Athena and Redshift Spectrum - Multiple engines: Supports both Apache Spark and Ray for processing - Visual job editor: No-code option for building ETL workflows - 70+ data connectors: Connects to diverse on-premises and cloud data sources

Amazon Redshift

Amazon Redshift is a fully managed, petabyte-scale data warehouse service. AWS describes it as providing fast query performance on large datasets with familiar SQL features. Redshift Serverless enables access without traditional provisioned configurations, with automatic resource provisioning and intelligent capacity scaling.

Key capabilities: - Columnar storage: Optimized for analytical queries - Massively Parallel Processing (MPP): Distributes queries across nodes - Redshift Spectrum: Query data directly in S3 without loading - Serverless option: Pay only when the warehouse is active - Native SQL transformations: Perform complex transformations using SQL

Amazon Athena

Amazon Athena is an interactive query service that enables analysis of data directly in Amazon S3 using standard SQL. According to AWS documentation, it requires no infrastructure setup and provides automatic scaling with parallel query execution.

Key capabilities: - Serverless: No infrastructure to manage - Pay per query: Charged based on data scanned - Standard SQL: Uses Presto/Trino under the hood - Glue Data Catalog integration: Query tables cataloged by Glue crawlers - Apache Spark support: Run Spark applications without managing resources

ETL vs ELT: Detailed Comparison

When to Choose ETL

ETL is the right choice when transformations must happen before data reaches the target system.

Best AWS tool combination for ETL: - AWS Glue for extraction and transformation - Amazon S3 for staging transformed data - Target system receives clean, transformed data

When to Choose ELT

ELT leverages the compute power of modern cloud data warehouses to transform data after loading.

Best AWS tool combination for ELT: - AWS Glue for extraction and loading (minimal transformation) - Amazon Redshift for storage and SQL-based transformations - Amazon Athena for ad-hoc queries on raw data in S3

Comparing Tool Combinations

Let's examine specific AWS service combinations for both ETL and ELT patterns.

ETL with AWS Glue + S3 + Athena

This combination uses Glue for heavy transformations before landing data in S3, where Athena queries it.

# Example: ETL transformation in Glue
from awsglue.transforms import *
from awsglue.dynamicframe import DynamicFrame
from pyspark.sql.functions import col, when, lit

# Extract from source
raw_data = glueContext.create_dynamic_frame.from_catalog(
    database="source_db",
    table_name="raw_events"
)

# Transform: Clean and enrich data
df = raw_data.toDF()
transformed_df = df \
    .filter(col("event_type").isNotNull()) \
    .withColumn("event_category",
        when(col("event_type").startswith("click"), lit("engagement"))
        .when(col("event_type").startswith("purchase"), lit("conversion"))
        .otherwise(lit("other"))) \
    .withColumn("processed_date", current_date())

# Load: Write transformed data to S3
transformed_frame = DynamicFrame.fromDF(transformed_df, glueContext, "output")
glueContext.write_dynamic_frame.from_options(
    frame=transformed_frame,
    connection_type="s3",
    connection_options={"path": "s3://bucket/processed/"},
    format="parquet"
)

Pros: - Serverless end-to-end; no infrastructure to manage - Cost-effective for infrequent or ad-hoc queries - Data is queryable immediately after Glue job completes - Supports open table formats (Iceberg, Hudi, Delta Lake)

Cons: - Query performance limited compared to dedicated data warehouse - Each Athena query scans S3; costs scale with data volume - No native support for complex aggregations or window functions - Limited join performance on large datasets

ETL with AWS Glue + Redshift

This combination uses Glue for transformations and loads the results into Redshift for high-performance analytics.

# Example: ETL to Redshift
from awsglue.transforms import ApplyMapping

# Extract and transform in Glue
raw_data = glueContext.create_dynamic_frame.from_catalog(
    database="source_db",
    table_name="sales"
)

# Apply schema mapping and transformations
mapped_data = ApplyMapping.apply(
    frame=raw_data,
    mappings=[
        ("order_id", "long", "order_id", "int"),
        ("amount", "double", "amount", "decimal(10,2)"),
        ("order_date", "string", "order_date", "date")
    ]
)

# Load transformed data to Redshift
glueContext.write_dynamic_frame.from_jdbc_conf(
    frame=mapped_data,
    catalog_connection="redshift-connection",
    connection_options={"dbtable": "analytics.fact_orders"},
    redshift_tmp_dir="s3://bucket/temp/"
)

Pros: - High-performance queries with Redshift MPP architecture - Complex SQL transformations happen in Glue (offloads warehouse) - Data arrives in warehouse ready for consumption - Predictable warehouse costs

Cons: - Transformation logic lives in Glue scripts (PySpark) - Schema changes require updating Glue jobs - Additional compute costs for Glue processing - Less flexibility to reprocess raw data

ELT with Glue + Redshift + Athena

This is the recommended pattern for most modern data warehouses. Glue handles extraction and loading, while Redshift performs transformations using SQL.

# Glue job: Extract and Load (minimal transformation)
raw_data = glueContext.create_dynamic_frame.from_catalog(
    database="source_db",
    table_name="events"
)

# Add load metadata only
df = raw_data.toDF()
df = df.withColumn("_loaded_at", current_timestamp()) \
       .withColumn("_source_file", input_file_name())

# Load to Redshift staging table
load_frame = DynamicFrame.fromDF(df, glueContext, "load")
glueContext.write_dynamic_frame.from_jdbc_conf(
    frame=load_frame,
    catalog_connection="redshift-connection",
    connection_options={"dbtable": "staging.raw_events"},
    redshift_tmp_dir="s3://bucket/temp/"
)

-- Transform in Redshift (SQL)
INSERT INTO analytics.fact_events
SELECT
    event_id,
    user_id,
    event_type,
    CASE
        WHEN event_type LIKE 'click%' THEN 'engagement'
        WHEN event_type LIKE 'purchase%' THEN 'conversion'
        ELSE 'other'
    END AS event_category,
    event_timestamp,
    EXTRACT(DATE FROM event_timestamp) AS event_date,
    _loaded_at
FROM staging.raw_events
WHERE _loaded_at > (SELECT COALESCE(MAX(_loaded_at), '1900-01-01') FROM analytics.fact_events);

Pros: - Raw data preserved in staging for reprocessing - SQL-based transformations are easier to maintain and test - Leverage Redshift's optimized SQL engine for complex transformations - Athena can query raw data in S3 for data validation

Cons: - Requires Redshift compute for transformations - Raw data consumes warehouse storage - Two-step process (load then transform) adds complexity

ELT with S3 + Athena (Lakehouse Pattern)

For teams prioritizing flexibility over query performance, transform data in S3 using Athena CTAS (CREATE TABLE AS SELECT).

-- Create transformed table using Athena CTAS
CREATE TABLE analytics.processed_events
WITH (
    format = 'PARQUET',
    external_location = 's3://bucket/processed/events/',
    partitioned_by = ARRAY['event_date']
) AS
SELECT
    event_id,
    user_id,
    event_type,
    CASE
        WHEN event_type LIKE 'click%' THEN 'engagement'
        WHEN event_type LIKE 'purchase%' THEN 'conversion'
        ELSE 'other'
    END AS event_category,
    event_timestamp,
    DATE(event_timestamp) AS event_date
FROM raw_data.events
WHERE event_timestamp >= DATE_ADD('day', -7, CURRENT_DATE);

Pros: - Fully serverless; pay only for queries - No data warehouse to provision or manage - Raw and processed data both in S3 - Works well with open table formats (Iceberg)

Cons: - Query performance lower than Redshift - CTAS creates new tables; updates require additional logic - Limited support for incremental processing - Cost can increase with large or frequent queries

Decision Matrix: Choosing the Right Approach

Use this matrix to guide your decision based on workload characteristics.

Recommendations by Use Case

Use Case 1: Ad-Hoc Analytics on Moderate Data

Recommendation: ETL with Glue + S3 + Athena

If you have moderate data volumes and infrequent queries, this serverless stack minimizes costs. Transform data in Glue and query with Athena.

Data Sources → Glue (Transform) → S3 (Parquet) → Athena (Query)

Use Case 2: Enterprise Data Warehouse

Recommendation: ELT with Glue + Redshift

For high-performance analytics with complex transformations, use ELT. Load raw data with Glue and transform in Redshift using SQL.

Data Sources → Glue (Load) → Redshift Staging → Redshift Transform → Analytics Tables

Use Case 3: Data Lake with Occasional Warehouse Queries

Recommendation: Hybrid with Glue + S3 + Athena + Redshift Spectrum

Store all data in S3, query with Athena for exploration, and use Redshift Spectrum for joining data lake with warehouse tables.

Data Sources → Glue (Catalog) → S3 (Data Lake)
                                    ↓
                              Athena (Ad-hoc)
                                    ↓
                              Redshift Spectrum (Join with DW)

Use Case 4: Real-Time Analytics

Recommendation: ETL with Kinesis + Glue Streaming + Redshift

For streaming data, use Kinesis Data Streams with Glue streaming ETL to transform and load to Redshift in near-real-time.

Streaming Sources → Kinesis → Glue Streaming → Redshift

Cost Considerations

Understanding cost implications helps inform your architecture decision.

AWS Glue Costs

Glue charges based on Data Processing Units (DPUs) used by your jobs. One DPU provides 4 vCPUs and 16 GB of memory. ETL jobs that do heavy transformations consume more DPUs than ELT jobs that simply load data.

Amazon Redshift Costs

Redshift offers two pricing models: - Provisioned: Pay for node hours regardless of usage - Serverless: Pay for compute used, measured in Redshift Processing Units (RPUs)

ELT pipelines that transform in Redshift use more warehouse compute but can eliminate external transformation costs.

Amazon Athena Costs

Athena charges per query based on data scanned. Using columnar formats (Parquet, ORC) and partitioning can reduce costs by scanning less data.

Best Practices

For ETL Pipelines

1. Use job bookmarks: Enable Glue job bookmarks for incremental processing
2. Optimize worker allocation: Start with G.1X workers and scale up only if needed
3. Partition output data: Partition by date or region to optimize downstream queries
4. Use columnar formats: Write to Parquet or ORC for better query performance

For ELT Pipelines

1. Use staging tables: Load raw data to staging before transforming to production tables
2. Implement incremental loads: Track loaded timestamps to avoid reprocessing
3. Leverage materialized views: Pre-compute common aggregations in Redshift
4. Use COPY for bulk loads: The COPY command is the most efficient way to load data into Redshift

General Best Practices

1. Catalog everything: Use Glue Data Catalog as a central metadata repository
2. Monitor data quality: Validate record counts and checksums between stages
3. Use infrastructure as code: Deploy pipelines with CloudFormation or Terraform
4. Implement alerting: Set up CloudWatch alarms for job failures and data freshness

Conclusion

ETL and ELT are not competing approaches—they serve different needs. ETL works well when transformations must happen before data reaches the target, especially for data quality, format conversion, or when targeting systems with limited compute. ELT shines when you want flexibility, raw data preservation, and the ability to leverage powerful data warehouse compute for transformations.

For most modern AWS data platforms, an ELT approach using Glue for extraction and loading combined with Redshift for SQL-based transformations provides the best balance of flexibility, performance, and maintainability. Athena adds value for ad-hoc queries on raw data and data validation.

Start with your requirements: query performance, data volume, transformation complexity, and team skills. Let these factors guide your choice rather than following trends.

Sources

1. What is AWS Glue? - AWS documentation describing Glue as a serverless data integration service with ETL/ELT capabilities
2. Amazon Redshift Database Developer Guide - AWS documentation on Redshift's data warehouse capabilities and serverless features
3. What is Amazon Athena? - AWS documentation on Athena's serverless, interactive query service
4. Amazon Redshift Data Loading Best Practices - AWS guidance on efficient data loading strategies including COPY commands
5. AWS Glue Data Catalog Integration - AWS documentation on using Glue Data Catalog as a Hive-compatible metastore