What Is an ETL Pipeline? Definition, examples, and use cases

An ETL pipeline is a data integration process that Extracts data from source systems, Transforms it into a suitable format, and Loads it into a target system such as a data warehouse. Modern ELT pipelines reverse the order by loading raw data first and transforming inside the warehouse. Key tools include Fivetran (managed EL), Apache Airflow (orchestration), dbt (transformation), and Airbyte (open-source EL).

What Is an ETL Pipeline? (2026)

Definition

An ETL pipeline is a data integration process that Extracts data from one or more source systems, Transforms it into a format suitable for analysis or operational use, and Loads it into a target system such as a data warehouse, data lake, or database. ETL pipelines are the foundation of data engineering, enabling organizations to consolidate data from disparate systems into a unified analytical environment.

The term "pipeline" refers to the sequential, automated flow of data through these three stages. ETL pipelines run on schedules (hourly, daily) or in response to events (new data arrival) and are managed by orchestration tools that handle dependencies, retries, and monitoring.

The Three Stages

Extract

The extraction phase reads data from source systems. Sources include relational databases (PostgreSQL, MySQL), SaaS application APIs (Salesforce, HubSpot, Shopify), flat files (CSV, JSON, XML), message queues (Kafka, RabbitMQ), and event streams. Extraction methods range from full table reads (simple but slow) to incremental extraction based on timestamps or change data capture (efficient for large datasets).

Transform

The transformation phase converts raw extracted data into the format required by the target system. Common transformations include:

Data cleansing: Removing duplicates, fixing formatting inconsistencies, handling null values
Data type conversion: Converting strings to dates, standardizing currency formats, normalizing units
Aggregation: Summarizing transaction-level data into daily, weekly, or monthly totals
Joining: Combining data from multiple sources using shared keys (customer ID, order number)
Business logic: Applying calculated fields, categorization rules, or derived metrics

Load

The loading phase writes transformed data into the target system. Loading strategies include full refresh (replace all data), incremental append (add new records only), upsert (insert or update based on key), and merge (apply inserts, updates, and deletes based on comparison with existing data).

Modern ELT vs Traditional ETL

Aspect	Traditional ETL	Modern ELT
Transform location	Staging server or ETL tool	Inside the target data warehouse
Compute resource	ETL platform's processing engine	Data warehouse's query engine (Snowflake, BigQuery, Redshift)
Raw data availability	Raw data is discarded after transformation	Raw data is stored and available for re-transformation
Flexibility	Transformations are defined before loading	Transformations can be modified after data is loaded
Latency	Higher -- transform before load adds time	Lower -- load raw data first, transform as needed
Cost model	ETL tool compute costs	Data warehouse compute costs
Typical tools	Informatica, Talend, SSIS	Fivetran (EL) + dbt (T), Airbyte + dbt

The ELT pattern has gained dominance since 2020, driven by the decreasing cost of cloud data warehouse compute. Fivetran popularized the "EL" (Extract-Load) approach, handling extraction and loading while delegating transformation to dbt (data build tool) running inside the warehouse.

Key ETL/ELT Tools (as of March 2026)

Tool	Approach	Starting Price
Fivetran	Managed EL with 500+ connectors	From $1/month per active row (estimated)
Apache Airflow	Open-source DAG-based orchestrator	Free (self-hosted)
Prefect	Python-native workflow orchestration	Free (open-source), Cloud from $0 (free tier)
dbt	SQL-based transformation framework	Free (Core), Cloud from $100/mo
Airbyte	Open-source EL with 350+ connectors	Free (self-hosted), Cloud from $0 (free tier)

Use Cases

Business intelligence: Extract data from CRM, marketing, finance, and support systems; transform into a unified data model; load into Snowflake for executive dashboards.
Customer 360: Combine customer data from multiple touchpoints (website, email, purchases, support tickets) into a single customer profile for personalization.
Regulatory reporting: Extract transaction data from banking systems, apply compliance transformations, and load into reporting databases for regulatory submissions.
Machine learning pipelines: Extract training data from production databases, transform into feature sets, and load into ML platforms for model training.