Clumio, Inc.

Oracle International Corporation

Comm Vault Systems, Inc.

Xia Hua

Woonho Jung

Rajeev Kumar

Lawrence Jaemyung Chang

Abdul Jabbar Abdul Rasheed

Douglas Qian

Shasank Kisan Chavan

John Stewart

Karan Dhabalia

Rolland Miller

Tirthankar Lahiri

Jesse Kamp

Roger Dermot Macnicol

Maria Colgan

Amit Ganesh

Juan R Loaiza

Kothanda Umamageswaran

Krishnan Meiyyappan

Allison Holloway

Hung Hing Anthony Pang

Viral Shah

Deepak Chawla

Adrian Tsz Him Ng

Yiran Qin

Kanwaljeet Sachdev

Michal Stanislaw Ostrowski

Richa Sehgal

Deepan Balajhi Saravanan

Maadhav Kaushal Shah

Nicholas Gerald Zehender

Chandan Sajjan Agarwal

Raghav Anand

Aniruddh Poornabodha Bharadwaj

Lawrence C Jenkins

Sangho Lee

Roger D Macnicol

Roger Macnicol

Disclosed techniques relate to security of backup data. In some embodiments, a method includes receiving, by data protection service running on a cloud computing system, a first encrypted copy of a backup of a first data store that is associated with a first account of an organization, where the first encrypted copy is encrypted using a first custodian cryptographic key that is shared between the organization and the data protection service that is different than a first production cryptographic key that is private and used by the organization to encrypt a non-backup version of the first data store. The method may include generating a second encrypted copy of the backup, including by encrypting the backup using a storage cryptographic key. The method may include storing the second encrypted copy of the backup in a second data store that is associated with the data protection service.

Data protection service using isolated, encrypted backup data

Techniques are disclosed relating to providing protection groups and rapid determination of expired objects and associated storage resources in a cloud-based backup storage context. In various embodiments, the disclosed techniques include generating, by a computing system, incremental backup data for a backup based on protection group information, including a set of cloud-based key-value data store buckets, filter information, and policy information. Disclosed techniques also include rapid determination of expired objects and associated storage resources. Disclosed techniques may advantageously provide a centralized view across buckets, accounts, and regions and allow users to target specific subsets of their data stores for backup, which may improve performance and reduce backup storage costs, relative to traditional techniques.

Protection groups for backing up cloud-based key-value stores

Techniques are disclosed relating to providing data views from a time-series data lake to a data warehousing system. In various embodiments, the disclosed techniques include providing, by a cloud-based service, a data lake service that maintains a time-series data lake storing a time-series representation of data from a plurality of data sources associated with a first organization. In some embodiments, the cloud-based service may receive additional backup data, including a first backup image of a first data source, associated with the first organization as part of a backup operation. The cloud-based service may then store a logical backup of the first data source in the data lake and, in response to a query from a data warehousing system, the cloud-based service may retrieve a particular view of the backup data from the data lake and provide it to the data warehousing system.

Providing data views from a time-series data lake to a data warehousing system

Techniques are disclosed relating to the storage of backup data using a time-series data lake. For example, in various embodiments, the disclosed techniques include providing a cloud-based data lake service that maintains data for a plurality of organizations and where, for a first organization, the cloud-based data lake service maintains a time-series data lake that stores a time-series representation of data associated with the first organization. In various embodiments, the data lake service may receive backup data from a plurality of data sources associated with the first organization, generate metadata associated with the backup data, and store the backup data, along with the corresponding metadata, in the time-series data lake.

Storage of backup data using a time-series data lake

Techniques herein use in-memory column vectors to process data that is external to a database management system (DBMS) and logically join the external data with data that is native to the DBMS. In an embodiment, a computer maintains a data dictionary for native data that is durably stored in an DBMS and external data that is not durably stored in the DBMS. From a client through a connection to the DBMS, the computer receives a query. The computer loads the external data into an in-memory column vector that resides in random access memory of the DBMS. Based on the query and the data dictionary, the DBMS executes a data join of the in-memory column vector with the native data. To the client through said connection, the computer returns results of the query based on the data join.

Performing in-memory columnar analytic queries on externally resident data

Techniques are disclosed relating to the modification of data in a time-series data lake. For example, in various embodiments, the disclosed techniques include a cloud-based service that maintains a time-series data lake that includes, for an organization, a time-series representation of data from one or more of the organization's data sources. The cloud-based service may receive a request to modify data associated with a particular user of the organization. As a non-limiting example, this request may correspond to a 'Right to Be Forgotten' request from the particular user. This request may include one or more search parameters and an indication of one or more modifications to be performed. Based on the request, the cloud-based service may parse the time-series data lake to identify a subset of data that matches the one or more search parameters and perform the requested modifications on the subset of data in the time-series data lake.

Modification of data in a time-series data lake

Techniques related to prefix compression are disclosed. Each value in a set of values has a prefix component and a suffix component. The set of values is divided into at least a first plurality of values and a second plurality of values. Compression of each plurality of values involves determining a prefix component that is shared by each value of the plurality of values. Compression further involves determining a plurality of suffix components belonging to the plurality of values. The prefix component is stored separately from the plurality of suffix components but contiguously with a suffix component of the plurality of suffix components. This enables the prefix component and the suffix component to be read as a value of the plurality of values.

Prefix compression

Techniques are provided for storing in in-memory unit (IMU) in a lower-storage tier and copying the IMU to DRAM when needed for query processing. Techniques are also provided for copying IMUs to lower tiers of storage when evicted from the cache of higher tiers of storage. Techniques are provided for implementing functionality of IMUs within a storage system, to enable database servers to push tasks, such as filtering, to the storage system where the storage system may access IMUs within its own memory to perform the tasks. Metadata associated with a set of data may be used to indicate whether an IMU for the data should be created by the database server machine or within the storage system.

Columnar caching in tiered storage

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Mountain View, CA, United States of America

Xia Hua