A data pipeline is a system designed to move and process data from one or more sources to a destination, often transforming it along the way. It automates the flow of data, ensuring it is reliably collected, cleaned, and made available for use. ETL (Extract, Transform, Load) is a specific type of data pipeline that follows a structured sequence: extracting data from sources, applying transformations (like cleaning or aggregating), and loading it into a target system like a data warehouse. While ETL is a well-established approach, data pipelines encompass a broader range of workflows, including real-time processing, streaming, or scenarios where transformations happen after loading (ELT).
Data pipelines and ETL overlap in purpose but differ in scope. ETL is typically batch-oriented and emphasizes transforming data before loading it into a structured storage system. For example, an ETL process might extract daily sales records from a database, calculate monthly totals, and load aggregated results into a reporting database. In contrast, modern data pipelines often handle more varied use cases, such as streaming sensor data from IoT devices into a cloud storage system with minimal transformation. Tools like Apache Airflow or AWS Glue can manage both ETL and other pipeline types, but pipelines might also use frameworks like Apache Kafka for real-time streaming or Apache Spark for distributed processing. This flexibility allows pipelines to support diverse requirements, such as low-latency analytics or machine learning data preparation.
The relationship between data pipelines and ETL becomes clear when considering use cases. ETL is ideal for structured, scheduled workflows where data quality and consistency are critical upfront—for example, migrating customer data from a legacy system to a new CRM. Data pipelines, however, can address scenarios like ingesting social media feeds for real-time sentiment analysis, where raw data is stored first and transformed later. Developers might use ETL tools for predictable batch jobs but rely on pipeline frameworks to handle unstructured data, scalability, or hybrid workflows. In practice, many systems combine both: an initial ETL stage to structure core data, followed by pipeline components for real-time updates or incremental processing. This hybrid approach balances reliability with adaptability.