Rubrik, Inc.

Ramya Thulasingam

Rohit Agrawal

David Anthony Terei

Prateek Pandey

Amelia Vu

Haitao He

Richard Yuh

Ningshan Li

Sujeet Gholap

A data management system (DMS) may use a staggered full snapshot rollout approach to obtain full snapshots of multiple computing objects. The DMS may identify that a quantity of the multiple objects scheduled for full backups by the DMS exceeds a threshold quantity. The DMS may initiate, at a first time, and based on the quantity of objects exceeding the threshold quantity, a first set of full snapshots corresponding to a first subset of objects included in the multiple objects. The DMS may initiate, at a second time subsequent to the first time, a second set of full snapshots corresponding to a second subset of objects included in the multiple objects. The second subset of objects may be different than the first subset of objects. The DMS may continue to obtain full snapshots of respective subsets of the multiple objects until all of the scheduled full snapshots are obtained.

Obtaining full snapshots for subsets of objects over time

Methods, systems, and devices for data management are described. A data management system (DMS) may determine to generate a backup instance of an application that includes multiple user accounts. The DMS may identify a time window for generation of the backup instance based on a set of backup parameters associated with the application. The DMS may calculate respective runtime durations for the user accounts based on respective quantities of data objects in the user accounts and respective backup job types associated with the data objects. The DMS may determine respective values of a semaphore parameter based on the respective runtime durations and the time window, where the semaphore parameter defines a threshold quantity of data objects to be processed in parallel. The DMS may generate the backup instance of the application based on processing the user accounts in accordance with the respective values of the semaphore parameter.

Dynamic resource scaling for large-scale backups

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Fremont, CA, United States of America

Ramya Thulasingam