What is an ETL Pipeline? - Definition & Meaning
Learn what an ETL pipeline is, how Extract/Transform/Load works with tools like Airflow and dbt, and why it is essential for data engineering.
Definition
An ETL pipeline (Extract, Transform, Load) is an automated process that extracts data from sources, transforms it into the desired format, and loads it into a target system such as a data warehouse. It forms the backbone of data engineering.
Technical explanation
The ETL process consists of three phases. Extract retrieves raw data from diverse sources: databases, APIs, file systems, SaaS applications, and event streams. Transform applies cleaning, normalization, aggregation, deduplication, and business logic to convert raw data into an analysis-ready format. Load writes the transformed data to the target system. ELT (Extract, Load, Transform) is a modern variant where raw data is first loaded into the data warehouse and transformations happen there using SQL, suited for powerful cloud solutions like BigQuery. Apache Airflow is the standard orchestrator: DAGs (Directed Acyclic Graphs) define task dependencies with scheduling, retries, and alerting. dbt (data build tool) focuses on the Transform step with SQL models, testing, and documentation. Fivetran and Airbyte automate the Extract and Load steps with pre-built connectors. Idempotent pipelines guarantee that repeated runs produce the same result. Data quality checks validate data for completeness, uniqueness, and consistency.
How MG Software applies this
MG Software builds ETL pipelines for clients who need to consolidate data from multiple sources. We use Airflow for orchestration and dbt for transformations. Pipelines are automated via schedules and monitored with alerting on failures. This enables our clients to have reliable, up-to-date data in their analytics environment.
Practical examples
- A marketing team building an ETL pipeline to combine daily data from Google Analytics, Facebook Ads, and their CRM into BigQuery for an integrated marketing dashboard.
- A fintech company using Airflow to extract transaction data nightly from multiple payment providers, normalize it, and load it into Snowflake for compliance reporting.
- An e-commerce platform using dbt models to transform raw order data into aggregated revenue metrics by product category, region, and time period.
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