Facebook, Inc.

Datacoral, Inc.

Meta Platforms, Inc.

Raghotham Murthy

Rajat Goel

Thomas Lento

Karthik Ranganathan

Adam Kramer

Nileema Bharat Shingte

Ankit Agarwal

Sameer Menon

Ragat Goel

Madhu Meethale Mallisseri

Vinesh Gudla

Namit Jain

Sambavi Muthukrishnan

Raveendra Bhat

Bhushan Mandhani

A method comprising receiving, from a first enterprise system, a series of first language commands written in a first language to control a first data flow of first data, the first data being from a first and a second data source, the first series of first language commands indicating first functions to be performed including collection of first data, storage of the first data into a first data warehouse, and application of one or more first applications on the stored first data, the first and second data sources being unrelated to each other and being remote from the first enterprise system and the data warehouse, translating the series of first language commands into first translated commands that may be provided to the first and second data sources as well as the first data warehouse to control the first data flow.

Systems and methods for generating, deploying, and managing data infrastructure stacks

Techniques for a system capable of performing low-latency database query processing are disclosed herein. The system includes a gateway server and a plurality of worker nodes. The gateway server is configured to divide a database query, for a database containing data stored in a distributed storage cluster having a plurality of data nodes, into a plurality of partial queries and construct a query result based on a plurality of intermediate results. Each worker node of the plurality of worker nodes is configured to process a respective partial query of the plurality of partial queries by scanning data related to the respective partial query that stored on at least one data node of the distributed storage cluster and generate an intermediate result of the plurality of intermediate results that is stored in a memory of that worker node.

System and methods for distributed database query engines

Generating, by a cloud-based system, a plurality of data infrastructure slices, each of the plurality of data infrastructure slices including a respective service; storing, by the cloud-based system, the plurality of data infrastructure slices; selecting, by the cloud-based server, at least two data infrastructure slices of the plurality of stored data infrastructure slices; generating, by the cloud-based system in response to the selection of the at least two data infrastructure slices of the plurality of data infrastructure slices, a data infrastructure stack comprising the selected stored data infrastructure slices, the data infrastructure stack capable of being executed in different third-party entity accounts of an on-demand cloud-computing platform; and deploying, by the cloud-based system, the data infrastructure stack in a particular third-party entity account of the on-demand cloud-computing platform.

The disclosed techniques provide systems and methods for anonymizing various portions of information, action logs, end-user information, and/or other data sets that are stored in non-indexed storage systems. More specifically, various anonymization procedures are described for redacting UII and/or replacing UII in raw data with randomly generated information (RGI). The anonymization process is performed on a rolling basis as raw data is received. An anonymization mapping table maps (or associates) the replaced UII in the anonymized data to the RGI, and eventually all raw data can be deleted.

Anonymizing user identifiable information

Some embodiments include a plurality of virtual data warehouses having table link capabilities that are built on top of a data center (e.g., running Apache Hive). Each virtual data warehouse can be modeled as a database and manage data in forms of database tables. The virtual data warehouse can include links which import tables from other virtual data warehouses by reference. Each link may contain partition metadata for the table partitions by dates of the source table and retention metadata to declare the needed retention time period for the partitions of the source table. The links can be dynamic and update when the corresponding source table receives new partitions or drops partitions. When a virtual data warehouse is migrated to another data center, the system can retain necessary table partitions on the current data center based on the partition and retention metadata of the links.

Hive table links

System and method for distributed database query engines

Growing community of inventors

San Francisco, CA, United States of America

Raghotham Murthy