Intelligent Cloud Portal Integration Flow Selection
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Solution Overview
Problem
The integration of data from on-premise systems to cloud portals is cumbersome, requiring manual setup, configuration, and deployment of various components, leading to complex onboarding efforts that can span multiple weeks, especially when choosing between integration patterns like event-based and delta-load architectures.
Innovation Solution
The implementation of intelligent cloud portal integration techniques that use automation and built-in intelligence to simplify the integration process through an assistant software, which selects the appropriate integration flow based on transaction record analysis, configuring services and APIs to replicate data from an on-premise database to a cloud platform, and dynamically switch between event-based and delta-load approaches to avoid bottlenecks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual setup and configuration of integration components is performed, then integration can be customized and controlled, but the onboarding time and complexity increase significantly
Solution Approach 1:
The system automatically analyzes transaction data patterns and selects appropriate integration flows without requiring manual user configuration. The integration process serves itself by autonomously determining volume thresholds, time intervals, and distribution patterns, eliminating the need for users to manually setup and configure integration components.
Solution Approach 2:
The system performs preliminary analysis of transaction records to determine volume thresholds and time intervals before the actual integration is executed. By pre-analyzing data patterns and pre-selecting appropriate integration flows, the system prepares everything in advance, reducing the time required during the actual onboarding process.
2Adaptability or versatility
If multiple integration patterns are available for selection, then the system can adapt to different data volumes and patterns, but the complexity of choosing and configuring the right pattern increases
Solution Approach 1:
The system continuously monitors transaction data patterns and uses this feedback to automatically select and adjust the appropriate integration flow. By analyzing actual data volumes, time intervals, and distribution patterns, the system receives feedback on which integration pattern is most suitable and adapts accordingly, eliminating the need for users to manually evaluate and select from multiple patterns.
Solution Approach 2:
The system changes parameters such as volume thresholds, time intervals, and distribution patterns based on the analyzed transaction data. By dynamically adjusting these parameters, the system adapts to different data volumes and patterns without requiring users to manually configure different integration patterns, thus maintaining adaptability while reducing complexity.
3Reliability
If data replication is performed continuously, then data freshness is maintained, but system resources are consumed constantly even when data volume is low
Solution Approach 1:
The system dynamically adjusts the data replication strategy based on actual transaction patterns. Instead of using a fixed continuous replication approach, the system modifies replication frequency and intensity according to observed data volumes, time intervals, and distribution patterns, ensuring data freshness is maintained while optimizing resource consumption.
Solution Approach 2:
The system implements periodic data replication based on determined time intervals rather than continuous replication. By analyzing transaction patterns to identify optimal intervals, the system performs replication periodically at appropriate moments, maintaining data freshness while avoiding unnecessary continuous resource consumption during low-activity periods.
Data Source
AI summary
Transaction records are obtained and respective volumes of the transaction records during a plurality of intervals are determined based on timestamps. A spread of a distribution of the respective volume is determined. Whether the respective volumes exceeds a predetermined volume threshold is determined. Then, an integration flow is selected from among a plurality of integration flows based on the respective volumes, the spread, and whether respective volumes exceeds the predetermined volume threshold. Services and application programming interfaces corresponding to the selected integration flow are configured. Then data is replicated from the database system to a cloud platform using the configured services and application programming interfaces. A portal application is provided to present the data on the cloud platform.