ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) describe two common approaches for moving data into a data warehouse. In ETL, data is transformed before it's loaded. With ELT, raw data lands in the warehouse first, and transformation happens there.
The shift to ELT reflects how modern cloud-native platforms work. They're faster, cheaper to scale, and flexible enough to process raw data on the fly. For teams dealing with siloed or high-volume datasets, these methods are essential to prep data for querying, modeling, or machine learning.
Key Difference between ETL and ELT
ETL transforms data before it enters the data warehouse. ELT loads raw data into the warehouse first, then transforms it using in-warehouse compute.
This affects:
- Performance: ELT leverages the warehouse’s compute layer for transformation.
- Cost: ELT can reduce data movement and use lower-cost storage for raw data.
- Governance: ETL allows stricter control before ingestion.
- Flexibility: ELT simplifies iteration, especially with schema-on-read.
Firebolt handles both ETL and ELT pipelines, as its high ingestion speeds, decoupled compute, and vectorized execution make it suited for ELT, where transformation runs inside the platform.
ETL: Extract, Transform, Load
Data is pulled from source systems. Transformation occurs outside the warehouse, using tools like Spark, dbt, or Python-based scripts. The final, cleaned dataset is then loaded into the target system.
ETL is common when transformation must happen before storage due to compliance, or when the destination lacks the processing power for complex transformations.
ELT: Extract, Load, Transform
Data is pulled and directly loaded into the warehouse in its raw format. All transformation takes place within the warehouse using SQL or integrated compute engines.
This method keeps raw data available for reprocessing, enables full audit trails, and handles evolving schemas. It's well-suited for modern platforms that can isolate and scale compute.
When Each Makes Sense
Use ETL for strict data validation before ingestion or when dealing with legacy infrastructure.
Use ELT when performance, schema agility, and in-database processing are more important.
Firebolt’s Fit
Firebolt handles raw data ingestion at speeds up to 10TB per hour. It integrates with external transformation pipelines and provides in-warehouse transformation capabilities through SQL, vectorized execution, and indexed data structures.
This gives teams the option to process data before or after it lands based on volume, latency, and architectural requirements.
Architecture and Performance: ETL vs ELT
How you design your data pipeline, ETL or ELT, impacts speed, cost, and infrastructure complexity.
ETL Architecture
ETL includes three parts: a source system, an external processing engine, and a data warehouse.
- Data must be moved, transformed, and then loaded before it can be queried.
- The transformation engine must be deployed, scaled, and monitored as a separate system.
- This adds more systems to manage and increases total processing time.
ELT Architecture
ELT moves data directly from the source into the warehouse.
- Transformations are run inside the warehouse using SQL and internal compute.
- This setup eliminates intermediate processing systems and shortens the time between extraction and analysis.
- It requires the warehouse to handle large query volumes and fast execution.
Performance Comparison
- ETL: Reduces demand on the warehouse but takes longer to make data available.
- ELT: Provides faster access to data for querying and modeling, but places higher demands on warehouse performance.
Firebolt Context
Firebolt separates storage, compute, and metadata into independent layers and makes it scalable.
- Data ingestion can run in parallel with querying and transformation.
- Query speed is maintained under high user load due to isolated processing and native indexing.
- This design handles high-frequency SQL transformations without delay.
Common Tools and Use Cases
Tool selection depends on how data moves through your environment. Some setups rely on external processing before storage. Others push raw data into the warehouse and transform it there.
ETL Tools
Examples: Apache NiFi, Talend, Informatica, SSIS
Best used when:
- Regulatory rules require pre-storage transformation
- Infrastructure includes on-prem systems or hybrid setups
- Schema is fixed and data arrives in regular batches
- Reducing warehouse compute usage is a priority
ELT Tools
Examples: dbt, Fivetran, Matillion, Airbyte, Rivery
Best used when:
- The stack is fully cloud-based
- Data includes nested or semi-structured formats
- Dashboards refresh frequently, or models are updated often
- Data is staged for machine learning or exploratory queries
Firebolt Context
Firebolt connects directly with ELT systems like dbt and Fivetran.
It ingests raw data at high speeds, which suits customer-facing dashboards and high-frequency query environments.
It also works with external systems for teams that still run transformations before loading.
TL vs ELT: Side-by-Side Comparison
Here's a side-by-side comparison of ETL and ELT across critical technical categories. Each row breaks down a core aspect, including processing location, latency, scalability, and typical toolsets.
Common Challenges
ETL and ELT each come with their own limitations. Understanding them upfront helps prevent downstream issues.
ETL challenges
- Longer pipelines: Data moves through multiple systems, each adding latency and increasing failure points.
- Extra systems to manage: Running and maintaining external transformation layers requires setup, scaling, and monitoring.
- Rigid schemas: Upstream schema changes often require manual pipeline rewrites and testing.
- Slow time-to-analysis: Data isn’t available for querying until all transformation steps complete.
ELT challenges
- Increased compute demand: All transformations run inside the warehouse, which raises processing requirements.
- Unexpected costs: Poorly optimized queries or wide scans can drive up warehouse usage charges.
- Governance pressure: Storing raw data increases exposure to access control, data lineage, and retention risks.
- Messy query logic: Analysts often deal with overlapping raw and cleaned tables, increasing complexity in joins and filters.
Firebolt context
Firebolt isolates query execution, ingestion, and transformation, so one task doesn’t delay another.
- It provides native controls to track and limit query resource usage.
- Indexing and aggressive filtering reduce the cost of working with large raw datasets.
- Access controls are granular and built for managing structured, semi-structured, or raw data at scale.
Best Practices for ETL and ELT Implementation
These practices help teams reduce errors, control cost, and stay flexible as pipelines evolve.
- Define data ownership early
Assign clear responsibility for extraction, transformation logic, and data quality. This avoids overlap and gaps as systems grow.
- Optimize for cost and latency
In ETL, decouple storage from compute so transformation systems can scale independently.
In ELT, track query usage by transformation stage to prevent excessive warehouse spending.
- Use modular SQL for ELT
Break down logic into stages like staging, transformation, and marts. Modular SQL simplifies debugging and helps teams reuse code across projects.
- Build for change
Use version-controlled tools like dbt. Choose flexible data formats and schema structures that support updates without downtime.
- Monitor and audit pipelines
Track lineage, schema changes, and failures. In ELT environments, monitor query latency and compute load to avoid saturation.
Firebolt and Modern Data Pipelines
Firebolt is built to handle data at speed and scale, whether transformation happens inside or outside the warehouse.
- Handles ETL and ELT setups
External tools can load into Firebolt after transformation. Firebolt can also ingest raw data and process it internally.
- Designed for ELT performance
Ingests up to 10TB per hour and executes queries with sub-second latency. Compute runs independently from storage and metadata layers.
- Simplified design
Built-in SQL engine with vectorized processing and indexing removes the need for OLAP cubes or manual aggregation.
- Developer-focused tools
Works with Postgres-compatible SQL, integrates with dbt, and includes SDKs for custom applications. Easy to slot into existing systems.
ETL vs ELT for Different Workloads
Can I use both ETL and ELT together?
Yes. Many teams extract and clean core data using ETL, then apply additional business logic with ELT in the warehouse.
Does Firebolt require ELT?
No. Firebolt works with both approaches. It supports high-throughput ingestion and performs just as well with pre-modeled or raw data.
Is ELT more expensive than ETL?
Not always. ELT can reduce infrastructure costs but may increase compute usage in your warehouse. Firebolt’s workload isolation and cost control tools help manage this.
If you're running ETL or ELT at any serious scale Book a demo to see how Firebolt handles raw data, SQL transformations, and high-concurrency queries without slowing down.
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