Cohesity, Inc.

Databricks Inc.

Abhijit Chakankar

Manoj Singhal

Warren Shen

Sashikanth Madduri

Prashant Gothi

Anubhav Gupta

Rupesh Bajaj

Suman Chandra Tokuri

Sunil Moolchandani

Prajakta Balkrishna Ayachit

Sandeep Tandekar

Vivek Velankar

Apollo Aguilan Tanting

Aditya Tandon

Itai Shlomo Weiss

Daniel Seth Morris

Stephen Michael Mahoney

William Chau

Dominic Tu Ping Cheah

Sai Madan Mohan Reddy Patlolla

Apurv Gupta

Ramesh Chandra

Xiaotong Sun

Pramesh Gupta

Vipin Vishvkarma

A data processing service receives indication that a recipient will request access to data assets of a provider and provides a request for credentials from a recipient governance module. The recipient governance module stores a recipient metastore including an object for a provider metastore. In response to determining that the assets are associated with the provider metastore, the service provides a request for credentials to a provider governance module. The provider governance module stores the provider metastore describing data assets of the provider and permissions for accessing data assets. The provider metastore includes a recipient object attached to the data assets with an identifier for the recipient metastore. In response to verifying that the recipient was provided access to the data assets, the service provides a token to the recipient governance module. The service then provides the token to a computing resource to provide access to the data assets.

Data asset sharing between accounts at a data processing service using cloud tokens

A data processing service facilitates the creation and processing of data processing pipelines that process data processing jobs defined with respect to a set of tasks in a sequence and with data dependencies associated with each separate task such that the output from one task is used as input for a subsequent task. In various embodiments, the set of tasks include at least one cleanroom task that is executed in a cleanroom station and at least one non-cleanroom task executed in an execution environment of a user where each task is configured to read one or more input datasets and transform the one or more input datasets into one or more output datasets.

Clean room generation for data collaboration and executing clean room task in data processing pipeline

A data processing service receives a request from a first collaborator to create a clean room for data sharing collaboration with at least a second collaborator. In response, the data processing service creates an execution environment separate from the data environment of the first collaborator and the second collaborator. The first and second collaborators can then add content into the clean room in the form of data tables and executable notebooks. Approval from each collaborator is required before a notebook can be executed using any data table shared into the clean room. Upon receiving notebook approval from each collaborator, the data processing service creates a notebook job to execute the notebook on one or more cluster computing resources of the data processing service to generate an output.

Clean room generation for data collaboration

A request to restore a database to a particular point in time is received. It is determined that a closest preceding backup to the particular point in time is an incremental backup. One or more transaction log file segments needed to restore the database to the particular point in time are determined. An updated incremental backup is generated by applying the one or more determined transaction log file segments to the incremental backup. The updated incremental backup is restored to a primary system.

Restoring a database using a fully hydrated backup

Events from one or more primary systems associated with one or more tenants are received. The received events are stored in a message queue. At least a portion of the events in the message queue are ingested for organization and storage in a data store. One or more progress identifiers associated with ingesting of the events in the message queue are tracked. An event query is received from an external system. The event query is rewritten into a first component query for the data store and a second component query for the message queue based at least in part on a progress identifier. A result of the first component query and a result of the second component query are combined to determine a result of the event query.

Utilizing progress identifiers to rewrite an event query

A request to restore an instance of an object that is stored in a storage associated with a cluster of nodes to a target destination is received. Data of the object is divided into a plurality of data portions. Corresponding data portions of the plurality of data portions are assigned to each node of a plurality of nodes of the cluster. It is determined that a first node of the cluster of nodes does not currently store in a storage associated with the first node at least a part of a corresponding data portion of the object assigned to the first node. At least the part of the corresponding data portion of the object assigned to the first node is received at the first node from a second node of the cluster of nodes. At least the part of the corresponding data portion of the object received from the second node is provided from the first node to the target destination.

Parallel restore of a large object

Data chunks sent to a content destination are tracked. It is determined whether content of a portion of an object to be sent to the content destination matches one of the data chunks previously sent to the content destination. In response to the determination that the portion of the object to be sent to the content destination matches one of the data chunks previously sent to the content destination, a source reference to the matching previously sent data chunk where the content destination can locally obtain the content for the portion of the object is provided to the content destination.

Reducing the impact of network latency during a restore operation

A backup of a database is determined to be performed. A backup of at least a portion of contents of a storage volume that includes data of the database is performed. The backup includes a step of creating a new metadata tree root node. Creating the new metadata tree root node includes copying from another metadata tree root node of a tree data structure corresponding to a previous backup instance, one or more references to one or more lower tier metadata tree nodes associated with the tree data structure corresponding to the previous backup instance.

A portion of contents of a database is received from a first server. The received contents of the database is stored in a secondary storage system that tracks changes between different backup versions of contents of the database. A request to migrate the contents of the database to a second server is received. A version of contents of the database is provided to the second server using the secondary storage system. The secondary storage system is configured to determine an amount of changes to the database content from one of the versions of the database content provided to the second server and the amount of changes is utilized in determining whether to quiesce the database hosted on the first server.

Efficient database migration using an intermediary secondary storage system

A request to migrate from a secondary storage system to a server a requested version of database content of a database backed up at the secondary storage system is received. A new version of the database content at the secondary storage system is instantiated. Functionality of the database is maintained including by allowing a database application hosted on the server to access the database content from the secondary storage system using the instantiated new version of the database content while migrating the requested version of database content from the secondary storage system to the server.

Growing community of inventors

San Jose, CA, United States of America

Abhijit Chakankar