Amazon Technologies, Inc.

Databricks Inc.

Naga Raju Bhanoori

Gokul Soundararajan

Ippokratis Pandis

Naresh Chainani

Sriram Subramanian

Pavel Sokolov

Eric Ray Hotinger

Frederick Ryan Johnson

William Michael McCreedy

Mohammad Foyzur Rahman

Vivek Ramamoorthy

Aditya Subrahmanyan

Jingyi Qing

Rahul Potharaju

Vijayan Prabhakaran

Dengfeng Li

Venu Gopal Nayar

Lin Ma

Himanshu Raja

Zachary Vaughn Schuermann

Kam Cheung Ting

Jintian Liang

Sushim Mitra

John Thomas

Monish Gupta

Hebatalla Mohamed Mohamed Aly Eldakiky

Rajesh Parangi Sharabhalingappa

Efficient superblock data structures are implemented for a data set. A superblock data structure, a log to commit changes to a superblock data structure, and an auxiliary data structure may be updated when an update to a data set is performed. The update may update respective location headers in the auxiliary data structure and the superblock data structure.

Efficient superblock data structures for database systems

Database data of a database service may be privately shared across provider network regions. A database producer can authorize an account of to access a database in another region of the provider network. The consumer of the database can associate the database in order to accept the permission to access the database. The database can then be created as an external table at a consumer database engine and metadata for the database privately shared between producer and consumer database engines that can be used to query the external table.

Privately sharing database data across provider network regions

Disclosed is a configuration for managing the organization of data tables in cloud-based storage. The configuration receives metrics for data processing operations on the data table. Metrics include at least one of a size of the data table, a size of each file in the data table, and metadata describing the data table. The configuration automatically executes a cost-benefit analysis based on the one or more metrics for each candidate maintenance operation in a plurality of candidate maintenance operations. The configuration automatically selects a maintenance operation from the candidate maintenance operations to automate based on the cost-benefit analysis of the one or more candidate maintenance operations. The selected maintenance operation is automated and scheduled on the data table.

Auto maintenance for data tables in cloud storage

Growing community of inventors

Union City, CA, United States of America

Naga Raju Bhanoori