What does the term "data pipeline" refer to in data science?

The term "data pipeline" refers to a series of data processing steps that effectively transform raw data into a format suitable for analysis and decision-making. In data science, a data pipeline encompasses the entire process of collecting, cleaning, processing, and storing data, ensuring that the information flows seamlessly from one stage to the next. This systematic approach allows organizations to automate repetitive tasks, manage data more efficiently, and derive insights from the data with consistency.

A data pipeline typically includes stages such as data ingestion (collecting data from various sources), data transformation (cleaning and reshaping data), and data storage (saving the processed data). By using well-defined pipelines, data scientists and analysts can focus on deriving value from the data rather than getting bogged down in manual processing tasks. Furthermore, data pipelines often integrate various tools and technologies that help in scaling and maintaining the quality and reliability of data flows.

This focus on a structured series of processing steps distinguishes the correct answer from other options that describe unrelated concepts in data handling and analysis.