An extract, transform, and load (ETL) data pipeline is an automated workflow that turns raw records from business apps and databases into report-ready tables. When those pipelines deliver complete, accurate data on schedule, teams can rely on their reports and forecasts.

Building a reliable ETL pipeline starts with planning each stage, from selecting data sources to monitoring performance in production. This guide covers six steps to building an ETL pipeline and explains when an extract, load, and transform (ELT) approach may be a better fit.

What is an ETL pipeline?

With an ETL pipeline, data from source systems is extracted, then transformed on a separate server, then loaded into a data warehouse or data lake. Every transformation is completed before the data reaches the destination, so the data warehouse only stores tables that already match models for reporting or operations.

Each run of the ETL process goes through three phases:

• Extract: The pipeline reads records from SaaS applications, relational databases, event streams, and flat files like CSV or JSON exports. Extracted data moves to a staging area for transformation.
• Transform: Transformation encompasses a wide range of actions: removing duplicate records, converting dates and currencies to standardized formats, flagging missing values, combining tables, pivoting tables, feature engineering, and more. 
• Load: The pipeline writes finished tables to target storage, like Google BigQuery or Amazon Redshift, where BI tools, predictive models, and AI can query them directly.

A transformation failure can stop the whole run because loading doesn’t typically begin until transformation finishes. This strict extract-then-transform sequence distinguishes ETL pipelines from other types of data pipelines.

ETL pipeline vs. data pipeline

Data pipelines and ETL pipelines are related, but the terms aren’t interchangeable. A data pipeline is any automated process that moves data between systems. An ETL pipeline is a type of data pipeline that always transforms records before loading them into a destination. ETL and other systems might factor into one data pipeline. 

6 steps to build an ETL data pipeline

How data engineers choose sources and extraction methods impacts an ETL pipeline’s reliability. Build an ETL pipeline that keeps running when source systems change with this approach.

1. Define your data sources and requirements

List every source and the tables or API objects each report requires. For each source, note how often records change and how quickly analysts need updates in the data warehouse. Before choosing an extraction method, confirm that every required field exists in a source and that each report has an owner who signs off on its definitions.

2. Choose your extraction method and tools

Match each source to an extraction method based on how the source system exposes its records. SaaS applications often share data through REST API calls, while databases support change data capture tools that only pull changed rows from the transaction log. Custom Python or SQL scripts give engineers full control, but the team owns every fix when a source API changes. Managed data pipeline tools like Fivetran use pre-built connectors so there’s no need for custom scripts.

3. Design your transformations

Write rules that prepare raw data for reporting by removing duplicate orders or joining CRM records with billing data, for instance. In an ELT pipeline, a data integration platform like Fivetran loads raw data before this step. Data engineers then run SQL models inside the warehouse with data transformation tools like dbt Core.

4. Load data into your destination

Choose a load strategy for each table. A full refresh replaces the whole table on every run, which suits small reference tables. An incremental merge uses each row’s primary key to update records that changed and add new ones. Match source and destination data types, seeing as mismatches can cause load errors or rejected records. After the first load, confirm that row counts in target storage match the source.

5. Orchestrate and monitor the pipeline

Map task dependencies in an ETL pipeline diagram that shows which tasks must finish before others begin. Schedule runs with an orchestration tool like Apache Airflow™ or Dagster so transformations don’t start until after extraction finishes. A renamed field in Salesforce can break a job mid-run because ETL pipelines depend heavily on the structure of their sources. Add alerts for failed runs and late data, or choose ETL pipeline tools with scheduling and alerts built in.

6. Test and validate data quality

Add automated tests for duplicate primary keys, null values in required fields, accepted values, and row counts against the source. Run the tests after every load, and block dashboard refreshes when a test fails. Move the pipeline to production once the full test suite passes on a historical load. A pipeline with tested transformations and active alerts handles source changes without missing rows in reports.

ETL pipeline examples

An ETL pipeline may work when the reporting model stays stable. Common ETL pipeline use cases include:

• Unified reporting: An ETL pipeline combines CRM and ERP records into one data warehouse with shared definitions, so finance and sales report one revenue figure in business intelligence dashboards.
• Legacy migration: During a move off an on-premises database, the pipeline cleans and remaps old records before they reach the new platform. Engineering teams shut down the old system once row counts match.
• Audit trails: Regulated teams use ETL to write data in the formats auditors require. Run logs record which transformation logic each batch used, so the compliance team can trace any reported figure to its source record.

ETL may be especially appropriate for highly regulated use cases where data must be anonymized or aggregated, or for reporting and audit trails that require finance teams and auditors to have cleaned, verified records before anyone runs a query.

ETL vs. ELT: Which approach fits your pipeline?

The difference between ETL and ELT boils down to where transformation takes place. ETL transforms data on a separate server before loading, while ELT loads raw data into the data warehouse and transforms it there with SQL.

A hybrid pipeline combines ETL and ELT in one workflow. For example, a data engineering team could mask personal data before it leaves a regulated source and transform the remaining data in the warehouse after loading.

Cloud data warehouses scale compute on demand, allowing teams to run transformations in the data warehouse instead of on a separate staging server. Teams that move from ETL to ELT typically gain:

• ‍Fewer failures: Extraction and loading can continue independently of a failed transformation, leaving raw data available for analysts to query.

• Faster changes: New reports ship without a pipeline rebuild, given that analysts edit a SQL model without touching extraction code.
• Easier automation: Managed connectors from platforms like Fivetran can handle many schema and API changes, which frees up engineers’ time.
• Flexible scaling: Cloud data warehouses can add compute for each workload, so teams don’t have to buy on-premises hardware to handle large data volumes.

While ELT has multiple advantages, ETL works for compliance-heavy workloads and legacy systems that can’t send raw data to the cloud. 

Automate your ETL data pipeline with Fivetran

Manually built pipelines can break when a source API or schema changes. Data engineers have to patch custom scripts after each failure, increasing maintenance costs.

Fivetran automates the extraction and loading processes so data teams can focus on transformation logic. Fivetran’s fully managed connectors extract and load data from SaaS apps and databases, so data engineers don’t have to write or maintain extraction scripts.

Then, Fivetran Transformations run dbt Core projects and prebuilt Quickstart data models in the data warehouse, giving analytics engineers reporting-ready tables. With integrated scheduling, dbt model runs start automatically after each connection sync completes, keeping transformed tables up to date as soon as new data arrives.

Cars24, a company that sells pre-owned vehicles, chose Fivetran instead of building its own pipeline. The company estimated an in-house build would need a team of five or six people working for about four months. Fivetran now replicates the company’s MySQL data into Snowflake, allowing analysts to run queries whenever they need to.

Start a free trial with Fivetran to spend less time on pipeline maintenance.

FAQ

Can you build an ETL pipeline in Python?

Yes. Python libraries, like SQLAlchemy, cover extraction and transformation, and an orchestrator like Apache Airflow schedules each run at a set interval. Engineers also own the maintenance, writing retry logic and updating the code whenever a source system changes.

What are ETL pipeline tools?

ETL pipeline tools are software platforms that automate parts of the ETL process, from pulling source data to writing finished tables into a data warehouse. The tools range from open-source frameworks that engineers host themselves to fully managed platforms that maintain connectors and schema changes.

Apache Airflow is a trademark of The Apache Software Foundation.

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