Cloud VM Data Migration Pipeline for Bulk Data Synchronization
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Solution Overview
Problem
Migrating data from one data platform to another is a complicated, time-consuming, and expensive process, particularly in reservoir operations where large amounts of data are involved.
Innovation Solution
A method and system utilizing a virtual machine on a cloud service provider subscription to migrate access control list (ACL) information, legal tags, and reference data, with a file-generic data migration pipeline, analyzing data types, and synchronizing changes across platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data migration methods are used, then data can be migrated from one platform to another, but the process becomes complicated, time-consuming, and expensive
Solution Approach 1:
The system performs preliminary actions by creating a hierarchical data model before migration to determine the correct order of ingestion. This pre-planning ensures that dependent data types are migrated in the proper sequence, preventing migration failures and reducing the need for retries, thereby completing the migration faster without sacrificing completeness
Solution Approach 2:
The patent introduces an intermediary migration orchestration layer that manages the complex migration process. This intermediary component coordinates between source and target platforms, handling data extraction, transformation, and loading while managing dependencies between different data types. This abstraction layer simplifies the overall process and reduces migration time by automating complex coordination tasks
2Reliability
If traditional data migration methods are used, then data can be migrated from one platform to another, but the process becomes complicated, time-consuming, and expensive
Solution Approach 1:
The migration system is segmented into distinct modular components: data model generation module, migration pipeline module, orchestration module, and validation module. Each module handles a specific aspect of the migration process and can be independently configured and executed. This segmentation reduces system complexity by breaking down the monolithic migration process into manageable, reusable components with clear interfaces
Solution Approach 2:
The patent creates a universal hierarchical data model framework that can represent multiple data types and their relationships in a unified structure. This universal model serves as a common language between different platforms, enabling the same migration infrastructure to handle diverse data formats and schemas without requiring platform-specific customization, thereby reducing overall system complexity
3Productivity
If data migration is performed without proper planning, then migration can be initiated quickly, but data type dependencies cause migration failures
Solution Approach 1:
The system performs preliminary analysis to generate a hierarchical data model that captures dependencies between data types before migration begins. This pre-computed model determines the correct ingestion order, allowing the migration to proceed smoothly without failures caused by attempting to migrate dependent data before its prerequisites. The preliminary planning enables fast execution while guaranteeing success
Solution Approach 2:
The migration system incorporates feedback mechanisms that continuously monitor the migration progress and validate data integrity at each stage. The hierarchical model provides feedback on which data types are ready for migration based on dependency satisfaction. This feedback loop ensures that migration failures are prevented by only proceeding when all prerequisites are met, maintaining high success rates even at high speeds
Data Source
AI summary
Systems and methods for data migration are provided. A method for migrating bulk data from a first data platform to a second data platform includes: providing a virtual machine (VM) on a cloud service provider subscription, migrating access control list (ACL) information, one or more legal tags, and reference data from the first data platform to the second data platform, using the VM, analyzing data types of the bulk data, generating a file-generic data migration pipeline from the first data platform to the second data platform, using the VM, fetching storage records for the bulk data from the first data platform using the VM, migrating the bulk data from the first data platform to the second data platform, using the VM, validating the migrated bulk data in the second data platform, and synchronizing the migrated bulk data in the second data platform with changes made since the migrating began.