Part of the Analytify BI Glossary, clear definitions of the business intelligence, analytics, and modern data stack terms that matter.
ELT (Extract, Load, Transform) is a modern data integration pattern where raw data is extracted from source systems, loaded directly into a cloud data warehouse, and then transformed inside the warehouse using SQL or tools like dbt — leveraging the warehouse’s compute power for transformations.
ELT inverts the classic ETL order, leveraging the compute power of modern cloud data warehouses (Snowflake, BigQuery, Databricks) to run transformations natively. This approach is faster to set up, easier to debug (raw data is preserved), and aligns with how dbt and modern data stack tools are designed.
The shift from ETL to ELT is one of the defining changes in the modern data stack. Almost every new data team in 2026 starts with ELT by default; ETL is reserved for specific compliance or legacy scenarios.
A typical ELT pipeline looks like:
raw.salesforce.accounts.The key architectural shift is that all transformations happen in the warehouse, not on separate ETL infrastructure. The warehouse is the single source of truth and the single compute engine.
A team uses Fivetran to copy raw Salesforce, Stripe, and HubSpot tables into Snowflake (Extract + Load). dbt models inside Snowflake then join, filter, and aggregate the raw tables to produce analytics-ready tables like analytics.monthly_revenue and analytics.active_customers (Transform). Analysts query the dbt-managed tables from BI tools like Analytify. The whole pipeline runs in a few minutes per refresh.
2026 ELT tool landscape:
The market-leading managed EL service. 300+ connectors, fully automated, premium pricing.
Open-source ELT alternative. Self-host (free) or use Airbyte Cloud (managed).
The dominant T in ELT. Open-source SQL transformation tool. Almost every modern ELT stack uses dbt.
Talend-owned mid-market ELT.
No-code ELT with real-time pipelines.
Real-time ELT with streaming-first architecture.
Cloud data warehouses became fast enough to handle transformations natively, eliminating the need for intermediate transformation servers. SQL is more accessible than proprietary ETL tools. dbt brought version control, testing, and documentation to in-warehouse transformations.
dbt handles the “T” in ELT: it transforms data already loaded into the warehouse. dbt pairs with extract/load tools like Fivetran or Airbyte to form a complete ELT stack.
Compute costs in the warehouse increase with ELT, but engineering time and ETL infrastructure costs typically decrease, often netting to lower total cost. For mid-market teams, ELT is usually 30-50% cheaper than equivalent ETL.
Yes, with streaming ELT tools like Estuary Flow, Materialize, or RisingWave. Traditional batch ELT runs every 15-60 minutes; streaming ELT runs continuously.
ELT moves data INTO the warehouse from source systems. Reverse ETL moves data OUT of the warehouse to operational tools (CRM, marketing automation). They are complementary in the modern data stack.
Yes — ELT depends on a cloud data warehouse fast enough to handle in-warehouse transformations. Snowflake, BigQuery, Databricks, and Redshift are all suitable.