ETL: definition, how it works and role in data analytics
Updated on February 22, 2026
Quick definition
ETL (Extract, Transform, Load) is a data integration process that extracts data from heterogeneous sources, transforms it to normalise and enrich it according to a target schema, then loads it into a destination system such as a data warehouse for analysis and reporting. ETL is the pipeline that feeds an organisation's analytics dashboards with consolidated, reliable data.
How it works
ETL consists of three interdependent phases.
Extract retrieves raw data from various sources:
- Relational databases (MySQL, PostgreSQL)
- REST APIs (Salesforce CRM, Sublim analytics, ERP)
- CSV/Excel files
- Streaming feeds (Kafka)
Transform is the most complex phase. It includes cleaning (removing duplicates, handling null values), normalisation (standardising date, currency and country-code formats), enrichment (computed dimensions such as quarter or customer segment) and identity reconciliation (merging a CRM record with an analytics event for the same user).
Load inserts the transformed data into the destination system: data warehouse (BigQuery, Snowflake, Redshift) or analytical database (ClickHouse for real-time metrics).
We distinguish ETL (transformation before load) from ELT (load first, transformation in the warehouse) — the latter being preferred with modern cloud data warehouses.
Why it matters
ETL is the invisible foundation that makes consolidated analytics reporting possible in an organisation. Without a reliable ETL pipeline, data remains fragmented in disparate silos, making any cross-channel or cross-system analysis impossible.
For a SaaS, a well-designed ETL allows consolidation of:
- Web analytics data
- Product events
- CRM data
- Financial transactions
...in a single data warehouse, the single source of truth for all decision dashboards.
How to improve or use it
- 1Document all your data sources and their schemas.
- 2Adopt consistent naming conventions and a shared data glossary.
- 3Implement data quality checks at each stage: row counts, value validation, anomaly detection.
- 4Use an orchestration tool such as Apache Airflow to monitor runs and alert on failure.
- 5Prefer the ELT approach with dbt for versioned and testable SQL transformations.
With Sublim
Sublim provides a REST API that lets you extract your analytics data to your data warehouse via automated ETL/ELT processes. You can retrieve session metrics, custom events and conversion data at the desired granularity (hourly, daily) and load them into BigQuery, Snowflake or ClickHouse.
Frequently asked questions
What is the difference between ETL and ELT?
In ETL, data is transformed before being loaded into the warehouse (transformation in a dedicated engine). In ELT, raw data is first loaded into the warehouse, then transformed in SQL directly inside the warehouse (using its compute power). ELT is preferred with modern cloud warehouses (BigQuery, Snowflake) because it is more flexible and scalable.
Are Fivetran and Airbyte ETL or ELT tools?
Fivetran and Airbyte are EL (Extract and Load) tools: they extract data from sources and load it raw into your warehouse, without business transformation. Transformation is then performed in the warehouse, often with dbt. They are sometimes referred to as ELT tools. They offer hundreds of ready-to-use connectors that simplify data ingestion from SaaS sources.
How often should an analytics ETL pipeline run?
Frequency depends on your data freshness requirement. For daily reporting dashboards, a nightly extraction is enough. For operational alerts or real-time dashboards, hourly or streaming extraction is needed. For end-of-month financial data, a monthly extraction may suffice. Adapt the frequency to processing cost and actual business need.
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