Cloud Data Warehouse Recommendations via On-Demand Collection Accounts
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Solution Overview
Problem
Cloud-based data warehousing systems face challenges in sharing and replicating data across different cloud platforms and regions due to native sharing protocols that prohibit data sharing between accounts associated with different organizations, leading to inefficiencies and security issues when managing and tracking costs.
Innovation Solution
The implementation of on-demand data collection accounts facilitates data sharing and replication across multi-cloud platform/region environments by instructing client accounts to share data with a second data manager account, which then replicates the data to a first data manager account, allowing for centralized data management and resource recommendation generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data sharing protocols are enforced across different cloud platforms and regions, then data accessibility and collaboration are improved, but security risks and compliance challenges increase
Solution Approach 1:
The patent implements a data loss prevention (DLP) intermediary system that sits between data sources and consumption points across cloud platforms. This intermediary actively monitors, controls, and manages data sharing requests, applying security policies and compliance rules to enable secure data accessibility across organizational and platform boundaries without compromising security posture.
2Measurement precision
If manual data tracking and cost management methods are used, then detailed control over data usage is achieved, but processing time and computing resource usage increase
Solution Approach 1:
The patent replaces manual, mechanical data tracking methods with automated machine learning-based tracking systems. The system automatically instruments data pipelines, collects usage metrics, and computes costs through automated processes rather than manual intervention, significantly reducing processing time while maintaining or improving measurement precision through consistent automated data collection and analysis.
Solution Approach 2:
The patent implements self-service data tracking where the system automatically monitors its own data usage, generates cost reports, and provides real-time visibility into data consumption patterns without requiring external manual tracking efforts. The automated system serves itself by continuously collecting, processing, and reporting usage data.
3Productivity
If automated recommendation systems are deployed, then resource optimization insights are provided in real-time, but computing resource usage increases
Solution Approach 1:
The patent applies partial action by implementing recommendation generation only for the most critical data warehouse parameters and performance metrics rather than analyzing all possible parameters. The system focuses computational resources on generating recommendations for high-impact areas such as resource allocation, query optimization, and cost management, rather than exhaustively analyzing every system parameter.
Data Source
AI summary
Methods, systems, devices, and computer-readable media used by a cloud data management system for collecting data from accounts hosted by a cloud-based data storage system on different cloud platforms or in different cloud regions of a cloud platform. Collection of data in the multi-cloud platform and/or multi-cloud region environments may be facilitated by the on-demand creation of one or more data collection accounts. Based on the collected data, one or more recommendations, notifications, or alerts associated with usage of the data storage system may be generated.