An aggregation pipeline in MongoDB is a framework for data processing, transforming, and analyzing data within your collections. Imagine it as a series of stages where each stage performs a specific operation on the data as it passes through.

Each stage takes the output of the previous stage as input and produces a new result. Common stages include:

*   **$match:** Filters documents based on criteria.
*   **$group:** Groups documents by specified fields, enabling calculations like sums, averages, and counts.
*   **$project:** Reshapes documents, adding, removing, or renaming fields.
*   **$sort:** Orders documents based on specified fields.
*   **$unwind:** Deconstructs array fields to output a document for each element.

These stages are chained together to create a pipeline that transforms raw data into meaningful insights. The output is a refined set of documents or calculated values. It's more efficient than retrieving all data and processing it in your application because the server performs these operations, reducing network traffic and potentially improving performance. It's analogous to SQL's `GROUP BY` combined with other operators, but much more flexible and powerful.
