Intelligent Load Plan for Multidimensional Database Data Integrity
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Solution Overview
Problem
In multidimensional database computing environments, existing load plans lack intelligence to detect and prevent situations where running the load could lead to data corruption or downtime, such as during system patching, upgrades, blackout windows, or concurrent load activities, resulting in potential data corruption and costly full warehouse reloads.
Innovation Solution
An intelligent load plan that automatically checks for and aborts the load process when conditions like system patching, source upgrades, scheduled blackout windows, or concurrent load plans are detected, ensuring the system remains in a runnable state and preventing data corruption by skipping or postponing data loads until conditions are favorable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the load plan runs automatically without intelligence checks, then productivity is improved by continuous data loading, but reliability deteriorates due to potential data corruption during patching or concurrent activities
Solution Approach 1:
The load plan performs preliminary intelligence checks before executing data loading operations. It detects system patching, upgrades, blackout windows, and concurrent load activities in advance, and aborts the load process when unfavorable conditions are detected, preventing data corruption before it occurs
Solution Approach 2:
The load plan continuously monitors system state and receives feedback about ongoing activities such as patching processes, upgrades, and concurrent loads. Based on this feedback, it dynamically decides whether to proceed with or abort the data loading operation, ensuring data integrity while maintaining productivity
2Reliability
If the load plan includes intelligence checks for system state, then reliability is improved by preventing data corruption, but device complexity increases due to additional detection mechanisms
Solution Approach 1:
The load plan performs self-diagnosis by automatically detecting system patching, upgrades, blackout windows, and concurrent load activities without requiring external intervention. It uses built-in intelligence to monitor its own execution environment and make autonomous decisions about whether to proceed with data loading
Solution Approach 2:
The load plan integrates multiple functions into a single unified process: it performs data loading, monitors system state, detects unfavorable conditions, and makes execution decisions. This multi-functionality reduces the need for separate monitoring systems and simplifies the overall architecture while improving reliability
3Reliability
If the load plan aborts when unfavorable conditions are detected, then data integrity is improved by preventing corruption, but loss of time occurs due to postponed or skipped loads
Solution Approach 1:
The load plan takes preliminary anti-action by detecting unfavorable conditions and aborting the load process before data corruption can occur. This prevents the need for costly full warehouse reloads and minimizes the actual time loss by only delaying loads when absolutely necessary
Data Source
AI summary
In accordance with an embodiment, an intelligent load plan that can automatically ensure that a system is in a runnable state and no other conflicting activity can affect the results of the data loads, such as ETLs. Such an intelligent load plan can be used in on data warehousing solutions as well as on a cloud data warehouse solution. The intelligent load plan can, in some embodiments, automatically detect situations based on which it knows it is not supposed to run the load plan.