Data aggregation in databases involves operations such as grouping, summing, and selecting distinct values, facilitated by various aggregation nodes like Aggregate, HashAggregate, and GroupAggregate. These nodes process the output of basic operations and are chosen based on factors like data size, query specifics, and memory availability. HashAggregate is used for unsorted outputs of GROUP BY, requiring significant memory, while GroupAggregate works with pre-sorted rows, demanding less memory. Parallel processing can enhance performance by distributing tasks among worker nodes, with results combined by a leader node using either sorting or hashing to eliminate duplicates. Indexing affects these operations by providing sorted data, which can optimize queries involving GROUP BY or SELECT DISTINCT. When memory is limited, the planner might switch from hashing to sorting, as illustrated in various examples using a flight tickets database. The article emphasizes the importance of understanding query plans and the role of sorting in optimizing data aggregation processes, setting the stage for a subsequent focus on explicit sorting in queries.