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Faster and more efficient data retrieval: By partitioning data based on specific criteria such as time or location, DolphinDB's database-oriented partitioning approach can vastly improve query performance and reduce the amount of data that needs to be analyzed.

Scalability: Partitioning can help distribute data across a larger number of physical disks or servers, allowing the database to handle larger volume of data and support more concurrent users.

Improved data management: Partitioning can make it easier to manage large datasets, allowing administrators to easily add or remove data based on specific criteria.

Reduced downtime: By dividing data across multiple servers, DolphinDB's partitioning approach can help minimize the impact of hardware failures, ensuring that data is always available.

Cost savings: Partitioning can lead to significant cost savings, as it allows organizations to store and process large amounts of data more efficiently, without the need for expensive hardware and infrastructure.