Clumio, Inc.

Amazon Technologies, Inc.

Comm Vault Systems, Inc.

Abdul Jabbar Abdul Rasheed

Rajeev Kumar

Xia Hua

Lawrence Jaemyung Chang

Woonho Jung

Douglas Qian

John Stewart

Karan Dhabalia

Rolland Miller

Jiangtao Zhang

Vivek Chawda

Ramu Panayappan

Javier Arturo Thiele-Ruiz

Mark Fawaz

Kanishk Kaul

Donghao Wei

Sreenaath Vasudevan

Emery Jacob Comstock

Pranav Prabhakar Firake

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 described for assisting users with the process of modernizing software applications from a source software architectural pattern (e.g., a model—view—controller (MVC) pattern, a multitier architecture, etc.) to a target architectural pattern (e.g., microservices, event-driven architecture, etc.). A decomposition analyzer receives as input artifacts associated with a software application under analysis and an architectural pattern that a user desires to use for a modernized version of the software application. The decomposition analyzer generates, based on the application artifacts, a graph-based model of the software application, where the model includes nodes representing application components (e.g., classes) and edges representing dependency relationships among the components. The decomposition analyzer further uses machine learning techniques and other types of analyses to identify recommended groupings of nodes from the graph-based model for decomposition. These recommendations can be displayed to users in a visualization providing decomposition assessment information.

Guided modernization of software applications using application templates

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 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

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San Jose, CA, United States of America

Abdul Jabbar Abdul Rasheed