Clumio, Inc.

John Stewart

Lawrence Jaemyung Chang

Rajeev Kumar

Xia Hua

Abdul Jabbar Abdul Rasheed

Woonho Jung

Douglas Qian

Karan Dhabalia

Rolland Miller

Rishabh Sharma

Hung Hing Anthony Pang

Woon Ho Jung

Poojan Kumar

Kaustubh Sambhaji Patil

Amber Palekar

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

A cloud-based data protection service is disclosed. In an embodiment, the data protection service may support backup of data sets from one or more sites associated with one or more organizations. In an embodiment, deduplication of backup data across multiple sites of an organization and/or multiple sites associated with different organizations may be supported. In an embodiment, backup data may be post-processed in the cloud to insert fingerprints corresponding to data blocks that did not change since a previous backup was performed, to scan the backup for security threats such as viruses, other malware, personally identifiable information, etc. In an embodiment, restore may be supported from the cloud, where restore blocks may be larger than backup data blocks. In another embodiment, restore may be based on blocks that have changed since the most recent backup (or a user-selected backup).

Cloud-based data protection service

Growing community of inventors

San Jose, CA, United States of America

John Stewart