Which Data Pipeline Wins? ETL vs. ELT
In the modern data world, two approaches dominate the construction of a data warehouse: ETL and ELT. Both extract, transform, and load data; the difference lies in the order and where the heavy lifting happens. Here’s a direct breakdown with a high-volume practical example.
ETL in a Nutshell
- Flow: Extract → Transform → Load
- How it works: You pull data from the source, transform it according to business rules, and then load it into destination tables.
- Where transformations happen: Outside the destination (ETL servers or dedicated engines), often using staging areas.
- Advantages: Fine control over data quality before loading, great for smaller loads and frequent updates.
- Common tools: SSIS, Informatica, Talend
ELT in a Nutshell
- Flow: Extract → Load → Transform
- How it works: You load raw data directly into permanent storage (data lake/warehouse) and then transform it later, leveraging the analytical engine’s power.
- Where transformations happen: Inside the warehouse/lakehouse using SQL or orchestrators.
- Advantages: Preserves full history, enables fast iteration, scales extremely well in the cloud.
- Common tools: Fivetran or Stitch for ingestion, dbt for transformations, Kafka/Firehose for near real-time events
Key Differences and Trends
| Aspect | ETL | ELT |
|---|---|---|
| When transformation happens | Before loading | After loading |
| Scalability | Limited by ETL servers | Cloud warehouses scale elastically |
| Cost model | Higher infra/maintenance costs | Cheaper storage + on-demand compute |
| Data freshness | Good for small, frequent updates | Supports CDC, incremental, and streaming |
| Best fit | Legacy on-premises, strict validation before load | Cloud-native, high-volume, experimental workloads |
- Market reality: Many legacy systems still rely on ETL, but most new projects lean toward ELT.
Mini Case: ELT with 10 Million Records
- Context: An app generates 10M events per day. Data is ingested via Fivetran into Snowflake (or BigQuery) in raw, date-partitioned tables.
- Transformation: Incremental dbt models apply business rules, deduplication, and enrichment on the daily partitions, leveraging clustering/partitioning for performance.
- Result: Analytics across months of history without full reprocessing every day. Raw data remains for auditing and time travel. Costs stay controlled by paying only for compute used.
- Note: For hot upserts (e.g., fixing a few thousand rows), incremental MERGE or streams/CDC are applied. If latency must be in seconds, add an event layer (Kafka/Kinesis) and materialized views.
Which One to Choose?
Choose ELT if:
- You’re building cloud-native systems from scratch.
- You need complete history and rapid experimentation.
- You handle massive volumes (millions to billions of rows) with variable peaks.
Stick with or choose ETL if:
- You’re running on-prem with strict dependencies.
- You need complex validations before loading or very predictable small batch latency.
- Your team/tools are already optimized for ETL stacks.
Suggested Tools and Stack
- Ingestion: Fivetran, Stitch, Airbyte; streaming with Kafka, Kinesis, or Firehose
- Storage / Compute: BigQuery, Snowflake, Databricks, Redshift, Lakehouse on S3/ADLS/GCS
- Transformation: dbt (SQL models, tests, lineage), notebooks for advanced use cases
- Orchestration: Airflow, Dagster, Prefect
- Observability / Data Quality: Great Expectations, Monte Carlo, Soda, OpenLineage
- BI & Consumption: Power BI, Tableau, Looker, Metabase
Conclusion
If you’re starting today, ELT usually wins thanks to scalability, flexibility, and lower total cost.
Still, ETL remains valid in legacy environments or when requirements demand tight pre-load control.
👉 The best choice depends on your sources, volumes, SLAs, and team capabilities.
Which one do you prefer, and why?