Cross-Cloud Data Pipeline Triggering via Change Data Capture
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Solution Overview
Problem
Conventional network-based database systems face challenges in efficiently sharing and synchronizing data across organizations and cloud platforms, particularly in real-time, due to latency issues associated with polling mechanisms.
Innovation Solution
The implementation of techniques that trigger data pipeline execution based on data changes (transaction commits) without relying on polling or external messaging mechanisms, utilizing change data capture (CDC) information and secure views to facilitate cross-organization and cross-cloud data sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If polling mechanisms are used to check for data changes, then data sharing can be achieved across organizations and clouds, but latency problems occur and real-time synchronization is compromised
Solution Approach 1:
The system performs preliminary action by capturing data changes at the source (transaction commit time) and immediately notifying subscribed pipelines. This is achieved through change data capture (CDC) mechanisms that monitor database transactions and trigger pipeline executions without waiting for polling intervals, thereby eliminating latency while maintaining reliable cross-organization data sharing
Solution Approach 2:
The system implements feedback by establishing a notification mechanism where data change events automatically trigger pipeline executions. Subscribed pipelines receive real-time notifications when source tables change, creating a closed-loop system that responds immediately to data changes rather than relying on periodic polling, thus resolving the latency issue while preserving data sharing reliability
2Speed
If data changes are captured and triggered immediately, then data synchronization speed is improved, but system complexity increases due to cross-cloud and cross-organization coordination requirements
Solution Approach 1:
The system uses an intermediary approach by introducing a standardized event notification mechanism that mediates between diverse data sources and subscribed pipelines. This intermediary layer handles the complexity of cross-cloud and cross-organization coordination through unified protocols and interfaces, allowing fast real-time triggering without requiring each system to directly manage complex peer-to-peer coordination
Solution Approach 2:
The system applies segmentation by separating the data change capture function from the pipeline execution function. Change data capture operates independently at the source system, while pipeline executions are triggered and managed separately through event subscriptions. This segmentation allows each component to operate independently at high speed without being burdened by the complexity of coordinating across organizational and cloud boundaries
Data Source
AI summary
Techniques for triggering pipeline execution based on data change (transaction commit) are described. The pipelines can be used for data ingestion or other specified tasks. These tasks can be operational across account, organization, cloud region, and cloud provider boundaries. The tasks can be triggered by commit post-processing. Gates in the tasks can be set up to reference change data capture information. If the gate is satisfied, tasks can be executed to set up data pipelines.