Definition, use cases, and comparison to data pipeline. This guide provides definitions, use case examples and practical advice to help you understand ETL pipelines and how they differ from data pipelines.
An ETL pipeline is a set of processes to extract data from one system, transform it, and load it into a target repository. ETL is an acronym for “Extract, Transform, and Load” and describes the three stages of the process.
Extract, Transform, and Load describes the set of processes to extract data from one system, transform it, and then load it into a target repository.
The ETL process is most appropriate for small data sets which require complex transformations. For larger, unstructured data sets and when timeliness is important, the ELT process is more appropriate (learn more about ETL vs ELT).
By converting raw data to match the target system, ETL pipelines allow for systematic and accurate data analysis in the target repository. So, from data migration to faster insights, ETL pipelines are critical for data-driven organizations. They save data teams time and effort by eliminating errors, bottlenecks, and latency to provide for a smooth flow of data from one system to the other. Here are some of the primary use cases:
Using ETL data pipelines in these ways breaks down data silos and creates a single source of truth and a complete picture of a business. Users can then apply BI tools, create data visualizations and dashboards to derive and share actionable insights from the data.
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The terms “ETL pipeline” and “data pipeline” are sometimes used synonymously, but they shouldn’t be. Data pipeline is an umbrella term for the category of moving data between systems and an ETL data pipeline is a particular type of data pipeline.
A data pipeline is a process for moving data between a source system and a target repository. More specifically, data pipelines involve software which automates the many steps that may or may not be involved in moving data for a specific use case, such as extracting data from a source system, then transforming, combining and validating that data, and then loading it into a target repository.
For example, in certain types of data pipelines, the “transform” step is decoupled from the extract and load steps:
Like an ETL pipeline, the target system for a data pipeline can be a database, an application, a cloud data warehouse, a data lakehouse, a data lake or data warehouse. This target system can combine data from a variety of sources and structure it for fast and reliable analysis.
Learn more about data pipelines.
Data pipelines also save data teams time and effort and provide for a smooth flow of data from one system to the other. But the broad category of data pipelines includes processes which can support use cases which ETL pipelines cannot. For example, certain data pipelines can support data streaming and here are examples of use cases based on data streaming:
Learn more about data pipelines.
The terms “data pipeline” and “ETL pipeline” should not be used synonymously. Data pipeline is the umbrella term for the broad set of all processes in which data is moved. ETL pipeline falls under this umbrella as a particular type of data pipeline. Here are three key differences when comparing data pipeline vs ETL.