Rubrik, Inc.

Snowflake Inc.

Lam Research Corporation

Betterworks Systems, Inc.

Di Wu

Subramanian Muralidhar

Benoit Dageville

Shreyas Narendra Desai

Matthew Edward Noe

Chieh-Sheng Wang

Shanthi Kiran Pendyala

Durga Mahesh Arikatla

Sameer Deshpande

Pui Kei Johnston Chu

Vishnu Dutt Paladugu

Shakhina Pulatova

Laxman Mamidi

Ziqi Xu

Biswaroop Palit

Ajay Saini

Oscar Annen

Chenyang Zhou

Peter K Loewenhardt

Nithin Mahesh

Kenji Takeshita

Sahaj Saini

Dean Jay Larson

Babak Kadkhodayan

Bi-Ming Yen

William M Denty, Jr

Xingcai Su

Khondokar Sami Iqram

Joshua Duncan Hight

Oscar Chen

Benjamin Reisner

Sanjay Srivastava

Christopher L Wong

Ciara Peter

Hitesh Madan

Stephanie Stillman

Timothy Hsin-Kang Chou

Boyu Charlie Tong

Christopher Wong

Kris Duggan

Khondokar Sami Igram

Paul Reeves

Xin Wen

Joyce Lam

Xinyue Liu

Li He

Robert K Chao

Loc Duc Bui

Some examples relate generally to computer architecture software for information security and, in some more particular aspects, to machine learning based on changes in snapshot metadata for anomaly and ransomware detection in a file system.

Anomaly and ransomware detection

Methods and systems for reducing the amount of time to restore a database or other application by dynamically generating and storing synthetic snapshots are described. When backing up a database, an integrated data management and storage system may acquire snapshots of the database at a snapshot frequency and acquire database transaction logs at a frequency that is greater than the snapshot frequency. In response to detecting that the database is unable to provide a database snapshot, the integrated data management and storage system may generate a synthetic snapshot of the database by instantiating a compatible version of the database locally, acquiring a previously stored snapshot of the database, applying data changes from one or more database transaction logs to the previously stored snapshot to generate the synthetic snapshot, and storing the synthetic snapshot of the database within the integrated data management and storage system.

Database recovery time objective optimization with synthetic snapshots

A process of fulfilling a database deployment request for a data platform. A compute service manager of the data platform scans one or more accounts of a consumer region of the data platform for a pending listing fulfillment request, where the pending listing request includes a request for deployment of a consumer database and an associated share of grant metadata of the consumer database within the consumer region. When the compute service manager determines that an account of the one or more accounts has a pending listing fulfillment request, the compute service manager determines a listing for the pending listing fulfillment request based on listing data of the account. The compute service manager determines that no other fulfillment task is scheduled for the pending listing fulfillment request and schedules a background fulfillment task to perform the fulfillment process for the pending listing fulfillment request.

Background job based refresh fulfillment

Techniques for database replication include a method performed by at least one hardware processor of a resource management node. The method includes detecting a request that a database instance of a database stored at a first network deployment is available at a second network deployment. Responsive to the request, the second network deployment is provisioned with a plurality of data objects. A first data object of the plurality of data objects includes a database shell. The database shell is reconfigured as the database instance of the database based on pushing data of the database into the database shell. Access to the database instance at the second network deployment is authorized based on at least a second data object of the plurality of data objects.

Database replication using a resource management node

A database system can configure network devices, such as a primary database in a multi-tenant deployment and a secondary database in a private deployment, to send and receive sequence messages, such as input data indicative of a selection of a link. The database system can create a secure share area in the private deployment in response to receiving the input data indicative of the selection of the link. The database system can replicate the data from the multi-tenant deployment to the secure share area in the private deployment and share the replicated data from the secure share area to the secondary database hosted in the private deployment.

Managing database traffic between isolated database systems

The subject technology determines a set of share objects in a replication group. The subject technology, for each object from the set of share objects, determines a set of permissions for a set of objects of databases that has been granted to each share object from the replication group. The subject technology, for each permission from the set of permissions, determines a set of dependencies, the set of dependencies comprising a second set of objects. The subject technology generates a graph structure based at least in part on the set of share objects, the set of permissions, and the set of dependencies. The subject technology traverses at least a portion of the graph structure to determine a final set of dependencies for replication. The subject technology performs a replication process based at least in part on the final set of dependencies.

Share-based replication

A method for secure data synchronization and sharing between distinct cloud environments is disclosed. The method involves establishing a trust relationship between a data provider's private cloud deployment and a data consumer's public cloud deployment. Utilizing hardware processors, a cross-region group sharing token is generated and validated to facilitate secure communication and data traffic between the two cloud deployments. Upon validation, a firewall policy that previously restricted data exchange is disabled, allowing for the replication of a target database from the private cloud to a secure share area accessible by the public cloud deployment. The data consumer is then enabled to access and utilize the replicated database within this secure area. The method ensures consistent data management across both deployments by managing the synchronization of the replicated database with the data provider's original database, thereby maintaining data consistency and integrity.

Bi-directional data sharing between private and public clouds

Some examples relate generally to managing and storing data, and more specifically to the real-time detection of ransomware, system (or insider) threats, or the misappropriation of credentials by using file system audit events.

Real-time detection of system threats

Various embodiments provide for replicating a share across deployments of a data platform, where the share can be on a source deployment and the share can be replicated on one or more target deployments, and where the share is replicated with one or more database objects of the source deployment associated with the share. Some embodiments analyze the share to be replicated and, based on the analysis, determine one or more database objects that would be replicated to the one or more target deployments to enable a replica of the share on the one or more target deployments.

Replication of share across deployments in database system

A data dictionary generation system automatically populates and updates a data dictionary for listings offering shared data. The data listing distribution component distributes the data dictionaries to various remote deployments in a data exchange by using a global messaging framework and replication method. For example, the data listing distribution component replicates a data dictionary generated for the listing and its shared data from a source deployment to one or more destination deployments associated with various geographic regions. The data listing distribution component distributes the listing to the various remote deployments to allow for the listing, including its shared data and data dictionary, to be accessed by users within the geographic region associated with the remote deployment.

Distributing large amounts of global metadata using object files

Some examples relate generally to computer architecture software for data classification and information security and, in some more particular aspects, to verifying audit events in a file system.

Data correlation using file object cache

A data dictionary generation system utilizes a background service that is programmed to automatically populate and update a data dictionary for listings offering shared data. A data dictionary includes metadata describing the shared data overall as well as the individual objects included in the listing, such as the individual tables, schemas, views, and functions. To generate the data dictionary, the data dictionary generation system analyzes the shared data to identify objects, identifies a set of data fields associated with each identified object and populates the set of data fields associated with each identified object based on the shared data offered by the listing. To ensure that a data dictionary for each listing remains up to date, the data dictionary generation system periodically scans the listings to identify any changes to share access granted to the listings.

Generating data dictionary metadata

Techniques for database replication for a remote deployment include a method performed by a data platform executing instructions on at least one hardware processor. The method includes detecting receipt of a database replication request associated with a data consumer. The database replication request is requesting availability of an instance of a primary deployment database at a remote deployment of a data platform. In response to detecting the receipt of the database replication request, an account of a data provider at the remote deployment of the data platform is provisioned with a set of replication-preparation objects associated with the primary deployment database. A database refresh operation is invoked, causing database data be pushed from the primary deployment database to a database shell in the account of the data provider at the remote deployment, to create the instance of the primary deployment database.

Database replication for a remote deployment

Techniques for database replication using replication preparation objects include storing, by at least one hardware processor, at least one task object at a remote deployment of a data provider. A replication request originating from an account of a data consumer is detected at the remote deployment using the at least one task object. The replication request is associated with a database available at a primary deployment of the data provider. Responsive to the detecting of the replication request, a local instance of the database is generated at the remote deployment. The account of the data consumer at the remote deployment is configured to access the local instance of the database.

Automated database replication using replication preparation objects

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Newark, CA, United States of America

Di Wu