What does the term “data pipeline” refer to?

The term "data pipeline" refers to a series of data processing steps that involve collection, processing, and analysis. In a data pipeline, raw data is ingested from various sources. This data then undergoes transformation and processing to prepare it for analysis or storage. Each step in the pipeline may involve different processes such as cleaning, filtering, aggregating, or enriching the data, eventually leading to insights that can be drawn from the analyzed results.

Data pipelines are essential in modern data engineering as they enable the movement and transformation of data from its origin to a designated storage location or analytical tool, ensuring timely and efficient data flow. This process supports a wide range of activities, including machine learning, business intelligence, and reporting.

In contrast, other definitions provide distinct purposes that do not capture the entire scope of a data pipeline. For instance, a sequence of data backups focuses solely on data preservation rather than the comprehensive processing and analysis accounted for in a pipeline. Additionally, methods for data visualization center on how data is presented rather than how it is processed, while a framework for data storage pertains to the organization and management of data rather than the operational flow that characterizes a data pipeline. Thus, option B comprehensively encapsulates the multi-step process central to the