ETL vs ELT for Analytics: Choosing the Right Data Integration Approach

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are two approaches to moving data from source systems into analytical platforms. The key difference is when transformation happens: ETL transforms data in transit before loading, while ELT loads raw data first and transforms within the target system.

Understanding this distinction matters because it affects architecture, cost, flexibility, and what's possible with your data.

The ETL Approach

How ETL Works

ETL processes data in three sequential stages:

Extract: Pull data from source systems - databases, APIs, files.

Transform: Cleanse, reshape, and enrich data in a transformation engine.

Load: Write transformed data to the target warehouse or database.

Source → Extract → Transform (in ETL tool) → Load → Target

Transformation happens outside the target system, often in dedicated servers or cloud services.

ETL Transformation Examples

Transformations in ETL include:

• Data type conversions
• Field concatenation and parsing
• Lookup enrichment from reference tables
• Aggregations and calculations
• Data quality filtering
• Format standardization

These operations complete before data reaches the warehouse.

Traditional ETL Tools

ETL tools provide visual or scripted transformation capabilities:

• Informatica PowerCenter
• IBM DataStage
• Talend
• Microsoft SSIS
• AWS Glue

These tools manage extraction, transformation logic, and loading.

The ELT Approach

How ELT Works

ELT reorders the process:

Extract: Pull data from source systems.

Load: Write raw data directly to the target warehouse.

Transform: Use target system's compute to transform data.

Source → Extract → Load → Transform (in warehouse) → Transformed Data

The warehouse handles transformation using SQL or DataFrame operations.

Why ELT Emerged

Cloud warehouses changed the economics:

Scalable compute: Snowflake, BigQuery, Redshift scale compute elastically.

SQL power: Modern SQL handles complex transformations efficiently.

Storage economics: Cheap storage makes keeping raw data practical.

Separation of concerns: Ingestion tools focus on reliable extraction.

When warehouses can transform efficiently, separate transformation tools become optional.

ELT Tools and Patterns

ELT uses specialized tools for each stage:

Extraction/Loading: Fivetran, Airbyte, Stitch handle ingestion.

Transformation: dbt, Dataform, SQL scripts handle warehouse transformations.

Orchestration: Airflow, Dagster coordinate the workflow.

The Codd AI Platform provides a semantic layer that works with both ETL and ELT architectures, ensuring consistent business definitions regardless of how data arrives in the warehouse.

ETL vs ELT Comparison

Transformation Location

ETL: Transformation happens in dedicated ETL servers or services.

ELT: Transformation happens inside the target warehouse.

Location affects what resources are used and what capabilities are available.

Data Preservation

ETL: Source data may be transformed away - only transformed data lands in warehouse.

ELT: Raw data preserved in warehouse - transformations create new tables from raw.

ELT provides flexibility to re-transform; ETL requires re-extraction for changes.

Latency

ETL: Transformation adds time before data is available.

ELT: Data lands quickly; transformation runs separately.

ELT can provide faster initial availability, though full transformation takes time either way.

Compute Costs

ETL: Pay for dedicated transformation infrastructure.

ELT: Pay for warehouse compute during transformation.

Cost comparison depends on workload and pricing models.

Flexibility

ETL: Transformations defined upfront; changes require ETL modification.

ELT: Raw data allows new transformations without re-extraction.

ELT provides more flexibility for evolving requirements.

Complexity

ETL: Single pipeline handles extraction through transformation.

ELT: Separate pipelines for ingestion and transformation.

ETL can be simpler for straightforward use cases.

When to Choose ETL

Data Sensitivity

When sensitive data shouldn't land in the warehouse:

• PII that must be masked before loading
• Data requiring encryption transformations
• Compliance requirements mandating pre-load processing

Pre-load transformation handles sensitive data safely.

Complex Non-SQL Transformations

When transformations exceed SQL capabilities:

• Machine learning feature engineering
• Complex string parsing and NLP
• Image or document processing
• Custom business logic in code

ETL tools can run arbitrary code during transformation.

Limited Target Capabilities

When the target lacks strong transformation support:

• Legacy databases with limited SQL
• Systems optimized for serving, not transforming
• Targets without compute scaling

ETL handles transformation elsewhere.

Performance Requirements

When transformation must complete before loading:

• Real-time requirements demand pre-transformed data
• Target systems can't handle transformation load
• Transformation workloads would impact query performance

Pre-load transformation keeps target systems focused on serving.

When to Choose ELT

Modern Cloud Warehouses

When targets have strong transformation capabilities:

• Snowflake, BigQuery, Redshift, Databricks
• Scalable compute for heavy transformations
• Strong SQL and DataFrame support

Leverage warehouse capabilities rather than building separate infrastructure.

Schema Flexibility

When source schemas evolve:

• Raw data landing handles schema changes gracefully
• Transformations adapt to new fields
• No extraction pipeline changes for additive changes

ELT isolates ingestion from transformation evolution.

Analytical Exploration

When analysts need raw data access:

• Data scientists explore raw data directly
• New analytical questions require different transformations
• Historical raw data enables retrospective analysis

Preserved raw data enables flexible exploration.

Development Velocity

When teams need to iterate quickly:

• Transformation changes don't require pipeline redeployment
• SQL transformations are easier to modify than ETL jobs
• Version-controlled transformations enable collaboration

ELT accelerates development cycles.

Hybrid Approaches

Pre and Post Transformation

Combine ETL and ELT:

• ETL for sensitive data requiring pre-load transformation
• ELT for general data that can land raw
• Different approaches for different sources

Match approach to specific requirements.

Tiered Transformation

Split transformation across stages:

• Light transformation during extraction (format normalization)
• Heavy transformation in warehouse (business logic)

Each stage handles appropriate work.

Real-Time and Batch

Different patterns for different latencies:

• ETL/streaming for real-time requirements
• ELT for batch analytical workloads

Latency requirements drive approach selection.

ELT Best Practices

Preserve Raw Data

Keep source data unchanged:

• Land in "raw" or "bronze" layer
• Transform into separate tables
• Maintain lineage to raw
• Enable re-transformation

Raw preservation provides flexibility.

Layer Transformations

Build transformation in stages:

Staging: Clean and standardize raw data.

Intermediate: Apply business logic and joins.

Mart: Create final analytical tables.

Layered transformations improve maintainability.

Test Transformations

Validate transformation quality:

• Schema tests for structure
• Business rule tests for logic
• Data quality tests for content

Testing ensures transformation reliability.

Version Control

Manage transformations as code:

• SQL in git repositories
• Code review for changes
• CI/CD for deployment

Version control enables collaboration and accountability.

ETL Best Practices

Design for Rerun

Build idempotent pipelines:

• Same input produces same output
• Reruns don't create duplicates
• State handled cleanly

Idempotency enables reliable operation.

Handle Failures Gracefully

Plan for problems:

• Checkpoint progress
• Enable partial reruns
• Log errors usefully
• Alert on failures

Graceful failure handling reduces operational burden.

Monitor Performance

Track pipeline health:

• Duration trends
• Resource utilization
• Data volumes
• Error rates

Monitoring enables proactive maintenance.

The Modern Consensus

Most modern analytics architectures favor ELT:

• Cloud warehouses handle transformation well
• Raw data preservation provides flexibility
• Specialized tools handle each stage
• Analytics engineering practices assume ELT

However, ETL remains relevant for specific use cases. Choose based on requirements, not trends.

Getting Started

Organizations choosing between ETL and ELT should:

1. Assess target capabilities: Can your warehouse handle transformation workloads?
2. Evaluate data sensitivity: Does data require pre-load processing?
3. Consider team skills: What tools does your team know?
4. Start with dominant pattern: ELT for modern warehouses, ETL for legacy or special requirements
5. Adapt as needed: Hybrid approaches are valid

The right approach depends on your specific context - technology, requirements, skills, and constraints.