Symantec Corporation

Veritas Technologies LLC

Symantec Operating Corporation

Veritas Operating Corporation

Kirk L Searls

Aaron Christensen

Gregory Robert Dowers, Ii

Ynn-Pyng “Anker” Tsaur

Ynn-Pyng Tsaur

Sunil Shah

Derek Dickinson

Ryan Schroeder

Weibao Wu

Srineet Sridharan

Pradip Kulkarni

Gaurav Makin

Baige Wang

Xianbo Zhang

Fanglu Guo

Michael Spertus

Hans Van Rietschote

Steven R DeVos

Michael A Payne

Sinh Nguyen

Christopher Greene

Suren Sethumadhavan

Lokesha B Krishnamurthy

Matthew Brocco

Check A Sabjan

Ravindra V Teli

Richard P Jones

Pu Patrick Ou

Arindam Panna

Tejas Mohan Sumant

Mohammed Eliyas Shaikh

Tejas Sumant

Patrick Ou

The disclosed computer-implemented method for preparing email databases for analysis may include (1) identifying an email database that stores a plurality of emails in a plurality of tables that are formatted to be managed by a specific email application, (2) using a component of the specific email application to retrieve the plurality of emails from the database, (3) creating a denormalized dataset for the plurality of emails by combining email data from at least one table from the plurality of tables with email data from at least one other table from the plurality of tables, and (4) exporting at least a portion of the data from the denormalized dataset into at least one file in an interoperable format that is capable of being read by a plurality of applications. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for preparing email databases for analysis

The disclosed computer-implemented method for updating email analytics databases may include (1) identifying an email database with a native format and an email analytics database that stores a copy of data in the email database in an analytics-friendly format that is denormalized relative to the native format of the email database, (2) capturing a log file comprising information that is about at least one recent change to the email database and that is formatted using the native format of the email database, (3) extracting the information about the recent change to the email database from the log file by transforming the information from the native format of the email database into the analytics-friendly format, and (4) updating the email analytics database to reflect the recent change to the email database by using the extracted information. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for updating email analytics databases

The disclosed computer-implemented method for automatically linking data analytics to storage may include (1) identifying a request to provision storage for a data analytics task, (2) collecting information relating to the data analytics task, the information comprising at least one of a data type of the data being used as input for the data analytics task and a characteristic of the data analytics task, (3) using a self-service provisioning tool to automatically compute, based on the collected information, a suggested type and size of data storage for the data analytics task, and (4) automatically provisioning data storage for the data analytics task based on the suggested type and size. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for automatically linking data analytics to storage

The disclosed computer-implemented method for building an extract, transform, load pipeline may include (i) identifying a plurality of ETL resources available to a user, (ii) categorizing each of the plurality of ETL resources based on at least one characteristic, (iii) provisioning the plurality of ETL resources for use with containers, (iv) presenting a user environment to the user, the user environment including a plurality of container images corresponding to available ETL resources, (v) receiving, from the user, a selection of at least one container image of the plurality of container images, and (vi) running at least one container from the at least one container image, the at least one container utilizing two or more ETL resources of the plurality of ETL resources. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for building an extract, transform, load pipeline

The present disclosure describes storing backup images in backup storage using snapshots. In response to receipt of a backup image that includes a complete copy of client data, a storage unit is generated and the backup image is stored in the storage unit. In response to receipt of a streamlined backup image that includes changed client data and metadata identifying unchanged client data in a base backup image, a base storage unit that includes the base backup image is identified, a snapshot is taken of the base storage unit (which generates a snapshot), and the streamlined backup image is stored on the snapshot. The metadata of the streamlined backup image is analyzed to identify portions of the base backup image that are not referenced by the streamlined backup image, which are removed from the snapshot.

Storing backup data using snapshots

The disclosed computer-implemented method for facilitating analytics on remotely stored data sets may include (1) identifying, within a secondary storage system, a secondary copy of a data set duplicated from a primary copy of the data set stored in a primary storage system, (2) generating a set of virtual objects that represent at least a portion of the secondary copy of the data set, (3) exposing the set of virtual objects to a remote analytics engine via a network such that the portion of the secondary copy of the data set appears to be stored locally on the remote analytics engine, and then (4) enabling the remote analytics engine to perform at least one analytics job on the portion of the secondary copy of the data set by way of the set of virtual objects via the network. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for facilitating analytics on remotely stored data sets

The disclosed computer-implemented method for completing sets of computing tasks may include (i) defining a set of computing tasks to be performed in a specified order, (ii) configuring a set of containers such that each container within the set of containers is configured to perform a task within the set of computing tasks, where each container isolates user space of a deployment system from other software on the deployment system while sharing kernel space with the other software, (iii) receiving a request to initiate the set of computing tasks, and (iv) completing, in response to the request, the set of computing tasks by launching the set of containers in the specified order. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for completing sets of computing tasks

The disclosed computer-implemented method for performing storage location virtualization may include (i) identifying a volume storage location for an operating-system-level virtualization container that isolates an instance of user space, the volume storage location providing a working space for data accessed during the existence of the operating-system-level virtualization container, (ii) mapping, through an automated driver that performs location virtualization, the volume storage location to multiple distinct source locations outside of the operating-system-level virtualization container, rather than a single source location, and (iii) propagating, through the automated driver, changes to the data within the working space of the volume storage location during the existence of the operating-system-level virtualization container to the respective multiple distinct source locations outside of the operating-system-level virtualization container to preserve the changes. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for performing storage location virtualization

The disclosed computer-implemented method for facilitating analytics on data sets stored in remote monolithic files may include (1) identifying, within a secondary storage system, a secondary copy of a data set duplicated from a primary copy of the data set stored in a primary storage system, (2) generating a set of virtual objects that represent at least a portion of the secondary copy of the data set, (3) exposing the set of virtual objects to a remote analytics engine via a network such that the set of individual data objects appears to be stored locally on the remote analytics engine, and then (4) enabling the remote analytics engine to perform at least one analytics job on the set of individual data objects by way of the set of virtual objects via the network. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for facilitating analytics on data sets stored in remote monolithic files

Techniques for performing an incremental backup are disclosed. In one particular exemplary embodiment, the techniques may be realized as a method for performing an incremental backup comprising retrieving a first identifier from a first portion of a prior backup of a database, backing up a first portion of a snapshot of the database, retrieving, using at least one computer processor, a second portion of the snapshot of the database, determining whether a second identifier of the second portion of the snapshot of the database is greater than the first identifier, and in the event the second identifier is greater than the first identifier, backing up the second portion of the snapshot of the database.

Techniques for performing an incremental backup

A method and apparatus for performing granular restoration from machine images stored on sequential backup media is disclosed. In one embodiment, the method for performing granular restoration from machine images stored on sequential backup media includes processing at least one machine image file to identify at least one database file and at least one data object that is arranged within the at least one database file and generating mapping information regarding the at least one data object within the at least one machine image file, wherein the at least one machine image is to be stored on the sequential backup media.

Method and apparatus for performing granular restoration of data objects from machine images stored on sequential backup media

A computer-implemented method for backing up virtual machine data may include (1) identifying a volume of data for a guest system within a virtual machine that may be scheduled for a backup from outside the guest system, (2) identifying, before the backup may be scheduled to occur, a plurality of blocks within the volume of data that have changed since a previous backup of the volume of data, (3) partially performing the backup by copying the plurality of blocks to a backup image and (4) completing the backup from outside the guest system once the backup may be scheduled to occur by copying to the backup image an additional plurality of blocks within the volume of data that have changed since identifying the plurality of blocks. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for backing up virtual machine data

A computer-implemented method for efficiently backing up data may include (1) identifying a volume scheduled for a backup by a computing system, (2) determining that resource usage of the computing system will fall below a predetermined threshold for a period of time, (3) identifying at least one unit of data in the volume of data to copy to a backup image for the backup during the period of time, (4) in response to determining that the resource usage of the computing system will fall below the predetermined threshold for the period of time, copying the unit of data to the backup image during the period of time, (5) taking the snapshot of the volume, and (6) copying at least one additional unit of data from the snapshot to the backup image to make the backup image consistent with the snapshot. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for efficiently backing up data

Various embodiments of a system and method for providing input/output virtualization for a file are disclosed. The system may include a filter program that intercepts a write request for a particular file and stores change information specifying the data and the write location indicated by the write request. However, the file itself is not modified. The filter program may also intercept a read request referencing the file and determine whether change information representing a write request to write data at the location specified by the read request has been previously stored. If so then the data specified by the change information is returned in response to the read request.

Virtualization of file input/output operations

A computer-implemented method for enabling deduplication of attachment files within a database is described. A database file comprising data blocks of an attachment file positioned intermittently among data blocks of the database file is inspected. A first map may be generated from the inspection of the database file and the attachment file. The data blocks of the database file and the data blocks of the attachment file are identified according to the first map. The data blocks of the database file are written to a database data file. The data blocks of the attachment file are written to an attachment data file. The attachment data file is deduplicated with at least one other data file.

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Maitland, FL, United States of America

Kirk L Searls