Connector Interface for Data Pipeline Workflow Management
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Solution Overview
Problem
The complexity of managing and provisioning large-scale computing resources in data centers, particularly with virtualization technologies, poses challenges in ensuring resource utilization, customer satisfaction, and revenue justification due to fluctuating resource pricing and complex task dependencies.
Innovation Solution
A data pipeline system with a scheduler and connector interface that allows for the creation, configuration, and execution of workflows across multiple computing nodes, enabling efficient data manipulation and storage, and scheduling of periodic tasks with complex dependencies, using a configurable workflow service and programmable connectors for data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If virtualization technologies are used to share computing resources among multiple customers, then resource utilization efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments computing resources into virtual machines that can be independently allocated and managed. Each virtual machine represents a discrete unit of computation that can be shared among multiple customers, enabling fine-grained resource utilization while maintaining manageable system complexity through modular organization.
Solution Approach 2:
The virtualization platform provides universal resource allocation capabilities that serve multiple customers with diverse needs through a single system. The same infrastructure can dynamically serve different workloads, customers, and resource requirements, improving overall utilization efficiency without proportionally increasing complexity.
2Loss of energy
If dynamic pricing is implemented for computing resources, then revenue optimization is improved, but resource allocation complexity increases
Solution Approach 1:
The system implements feedback mechanisms that monitor resource utilization, demand patterns, and pricing effects in real-time. This feedback loop enables dynamic pricing adjustments that optimize revenue while automatically managing allocation complexity through data-driven decision-making rather than manual intervention.
Solution Approach 2:
The resource allocation system transitions from static to dynamic pricing and allocation mechanisms. Pricing and resource distribution automatically adjust based on current demand, utilization metrics, and business objectives, enabling revenue optimization while the system self-manages the complexity of dynamic allocation through automated control.
3Productivity
If multiple data sources with complex dependencies are integrated into a data pipeline, then data processing capability is improved, but workflow management complexity increases
Solution Approach 1:
The system introduces intermediary components such as workflow engines, data pipelines, and integration layers that mediate between multiple data sources with complex dependencies and the processing operations. These intermediaries abstract the complexity of coordination and dependency management, enabling enhanced data processing capability while shielding users from workflow management complexity.
Solution Approach 2:
The system extracts and isolates complex workflow management logic into separate, dedicated components and services. By taking out the complexity of coordinating multiple data sources and their dependencies into specialized workflow engines, the main data processing operations can focus on their core functions while the extracted workflow management layer handles the coordination complexity.
Data Source
AI summary
Methods and systems for a connector interface in a data pipeline are disclosed. A pipeline comprising two data source nodes and an activity node is configured. Each data source node represents data from a different data source, and the activity node represents a workflow activity that uses the data as input. Two connectors which implement the same connector interface are triggered. In response, data is acquired at each connector from the corresponding data source through the connector interface. The data is sent from the connectors to the activity node through the connector interface. The workflow activity is performed using the acquired data.