What is meant by a ‘data pipeline’?

A 'data pipeline' refers to a series of steps for collecting, processing, and delivering data from its source to a destination where it can be analyzed or used for decision-making. This concept encompasses the entire flow of data, starting from the extraction of raw data from various data sources, followed by transformation processes such as cleaning, aggregating, or enriching the data, and finally loading it into a database or data warehouse for further analysis.

In building a data pipeline, it is crucial to ensure that the data is processed in a way that maintains its integrity and quality, and that it is delivered in a format suitable for analytics. This structured approach allows organizations to automate and streamline data operations, making it easier to handle large volumes of data and ensure timely access to high-quality information.

The other options refer to important aspects of data management but do not capture the comprehensive concept of a data pipeline. For example, a storage system, while essential for holding large datasets, does not describe the end-to-end process of data flow. Similarly, methods for visualizing data and tools for cleaning data are specific activities or tools within the broader data pipeline framework but do not encompass the entirety of data collection and processing.