What is a data pipeline, and how do you build one?

A data pipeline is a software system designed to ingest data from various sources, transform it as necessary, and move it to specific destinations, ensuring the data meets the requirements of the receiving systems. Companies use data pipelines primarily to consolidate data for analytics purposes, allowing analysts to work with a unified dataset without impacting the performance of production databases. While some advanced databases offer built-in features that mimic pipeline functions, separate data pipelines are often required to handle complex transformations and integrate data from multiple sources. Data pipelines can be categorized into batched or streaming types, with the former being more reliable for non-time-sensitive tasks and the latter essential for real-time data needs, such as providing quick recommendations in a video streaming service. The architecture of a data pipeline varies, typically involving steps like connection, extraction, cleaning, transformation, and export, but can differ depending on whether the pipeline follows ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes.

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