Cross-Cloud Data Pipeline Replication with Committed Versions
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Solution Overview
Problem
Existing database systems fail to efficiently replicate recurrently executing data pipelines across multiple geographic locations, vendors, and computing devices, leading to incomplete versions and loss of data continuity due to unsuitable transactional changes and versioning issues.
Innovation Solution
A method for replicating recurrently executing data pipelines by detecting a committed version and explicitly designating it for execution, allowing users to decide when changes are complete, ensuring a consistent version is available on a secondary pipeline, and supporting cross-account, organization, and cloud provider boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated replication of data pipelines is implemented across cloud services, then productivity and data continuity are improved, but version skew and reliability issues worsen due to unsuitable transactional changes
Solution Approach 1:
The system performs preliminary version tagging and commitment status detection before replication occurs. By identifying committed versions in advance and preparing replication packages with version metadata, the system ensures that only consistent, approved versions are replicated, preventing version skew between primary and secondary pipelines.
Solution Approach 2:
The system implements feedback mechanisms where version information and commitment status from secondary pipelines are continuously monitored and compared against primary pipeline states. This feedback loop enables automatic detection of version drift and triggers corrective replication actions to maintain consistency across distributed pipelines.
2Manufacturing precision
If explicit version designation for execution is required, then manufacturing precision of pipeline versions is improved, but device complexity increases due to additional version management overhead
Solution Approach 1:
The system creates simplified copies of version metadata and commitment information that can be easily replicated across cloud services. Instead of managing complex transactional states, the system copies version tags and commitment statuses, which are lightweight data structures that maintain precision without adding significant complexity to the replication mechanism.
Solution Approach 2:
The system changes the parameters of version management from complex transactional states to simple version identifiers and commitment booleans. This parameter transformation reduces the complexity of tracking pipeline versions while maintaining the precision needed to identify committed versions for replication.
3Adaptability or versatility
If cross-cloud replication is implemented, then adaptability and data availability are improved, but loss of information increases due to version skew between deployments
Solution Approach 1:
The system introduces version metadata and commitment information as intermediary elements between primary and secondary pipeline deployments. These intermediaries carry version identification and consistency status across cloud boundaries, ensuring that replication operations preserve version continuity and prevent information loss despite distributed deployment flexibility.
Data Source
AI summary
In one aspect, a computer-implemented method includes detecting a committed version of recurrently executed tasks of a first data pipeline on a primary deployment that is hosted on a first cloud service, and replicating the committed version of the recurrently executed tasks to a second data pipeline on a secondary deployment that is hosted on a second cloud service.