A data pipeline is a set of processes or tools that move data from one place to another. Data pipelines are used to transfer data from a source system to a destination system, typically for the purpose of analysis, transformation, or storage.

Data pipelines can be simple or complex, depending on the amount and type of data being transferred, the number of systems involved, and the complexity of the transformations being performed. In general, a data pipeline consists of the following components:

• A source system: This is the system from which the data is being extracted. The source system could be a database, a file system, a cloud storage service, or another type of data repository.
• A destination system: This is the system to which the data is being transferred. The destination system could be a database, a file system, a cloud storage service, or another type of data repository.
• One or more data connectors: These are the tools or processes that extract the data from the source system and load it into the destination system. Data connectors can be custom-built or commercial tools, depending on the requirements of the pipeline.
• One or more data transformations: These are the processes or rules that are applied to the data as it is being transferred from the source system to the destination system. Data transformations can include filters, aggregations, transformations, and other types of operations.

Data pipelines are commonly used in data analytics, data warehousing, and other scenarios where data needs to be transferred between systems. They can help automate the movement of data, ensure data integrity and quality, and enable real-time or near-real-time data processing.

For more information about data pipelines and how they work, see the https://en.wikipedia.org/wiki/Data_pipeline