Rubrik, Inc.

Manjunath Chinni

Rajorshi Kumar Sen

Noel Moldvai

David Anthony Terei

Sagar Kashinath Honnungar

Gabriel Adrian Lam

Travis Willem Morrissey

Jiangbin Luo

Abhishek Kumar

Sai Kiran Katuri

Nicholas De Leon

Kamna Jain

James Irvin

Prabudas Varadarajan

Veera Venkata Naga Rama Satyam Kandula

Van Hoang Thuy Nguyen

Swapnil Bawaskar

Hang Yang

Manjunatha Devegowda

Jonathan Chon

Methods, systems, and devices for data management are described. One or more requests to apply data observation to one or more data sources may be received via an interface. Whether snapshots are supported for the one or more data sources may be determined. A first, snapshot-supported data source may be stored at a first data storage as a snapshot and a representation of the second, snapshot-unsupported data source may be stored at a second data storage. First data may be extracted from the snapshot and second data may be extracted from the representation of the second data source such that a first data observation procedure may be applied to the first data and a second data observation procedure may be applied to the second data. Results of the data observation procedures may be reported via an interface.

Supporting non-snappable data sources

Methods, systems, and devices for data management are described. A client of a backup and recovery system may recover a virtual machine (VM) within a private network based on a previously-obtained snapshot of the VM. The VM may host a backup and recovery system for one or more computing objects within the private network prior to a disaster scenario. The backup and recovery system may recover, in response to a trigger, infrastructure of the backup and recovery system based on the snapshot. The infrastructure may include components and core services that operate the backup and recovery system. The core services may be operable to execute independent of other services. The backup and recovery system may subsequently recover data associated with one or more second services provided by the backup and recovery system. The second services may depend on at least one of the core services for execution.

Disaster recovery for private-network data backup and recovery systems

Methods, systems, and devices for data management are described. A database server may identify workload metrics for a set of database instances and generate a weighted sum of the workload metrics for each database distance. The database server may select, for a resharding operation, a first set of database instances and a second set of database instances, and the selection may be based on a relationship between a first sum of one or more respective weighted sums for the database instances in the first set and a second sum of one or more respective weighted sums for the database instances in the second set. The database server may execute the resharding operation that results in the first set continuing to be supported by the first database server and the second set being supported by the second database server.

Workload inspired input selection of databases for resharding

Methods, systems, and devices for data management are described. A first data source may be identified to move from a source cloud deployment (that operates in accordance with a first release cadence for updating database schema) to a destination cloud deployment (that operates in accordance with second, different release cadence for updating database schema). The first source database is then migrated from the source cloud deployment to an intermediate cloud deployment hosted on the source cloud deployments that allows the schema of the first data source to remain unchanged for a duration of time that the first data source is hosted on the intermediate cloud deployment. The database schema associated with the first data source is then updated to an updated (most recent) database schema in accordance with the second release cadence of the destination cloud deployment, and migrated from the intermediate cloud deployment to the destination cloud deployment.

Techniques for handling schema mismatch when migrating databases

Methods, systems, and devices for data management are described. A database instance associated with managing data of one or more users may be generated. In the database instance, a table of a database for a user of the one or more users may be generated. The table may be configured to store metadata for managing data of the user and may include a partitioning category. Based on generating the table, a file that indicates a policy for partitioning the table and for retaining partitions in the table may be identified. The policy may be based on a data retention policy associated with the data of the user. Based on identifying the file, partitions for the table may be generated in accordance with the partitioning category and the file, and based on generating the partitions, one or more partitions of the partitions may be deleted in accordance with the file.

Autonomous table partition management

Methods and systems for improving data back-up, recovery, and search across different cloud-based applications, services, and platforms are described. A data management and storage system may direct compute and storage resources within a customer's cloud-based data storage account to back-up and restore data while the customer retains full control of their data. The data management and storage system may direct the compute and storage resources within the customer's cloud-based data storage account to generate and store secondary layers that are used for generating search indexes, to generate and store shared space layers and user specific layers to facilitate the deduplication of email attachments and text blocks, to perform a controlled restoration of email snapshots such that sensitive information (e.g., restricted keywords) located within stored snapshots remains protected, and to detect and preserve emails that were received or transmitted and then deleted between two consecutive snapshots.

Data management system with limited control of external compute and storage resources

Methods, systems, and devices for data management are described. In some systems, a client may request for a data management system (DMS) to execute a first instance of a query to obtain, from a database, first information that is responsive to the query. The DMS may identify, in response to the query being executed in an execution time that is greater than a threshold amount of time, an improved version of the query that is associated with a second execution time that is less than the threshold amount of time. The DMS may store the improved version of the query. The DMS may retrieve the improved version of the query in response to receiving a second instance of the query. The DMS may execute the improved version of the query after retrieving the improved version from storage to obtain, from the database, second information that is responsive to the query.

Automatic query optimization

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Saratoga, CA, United States of America

Manjunath Chinni