Federated Query Quality Augmentation to Reduce Repeated Data Pulls
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Solution Overview
Problem
Existing federated query engines are time-consuming and resource-intensive due to repeated data pulling from disparate remote data sources, and they often provide inaccurate and outdated data quality metrics.
Innovation Solution
Implement on-demand data quality evaluation and augmentation techniques within federated query systems, allowing for parallel data quality assessments during query resolution, thereby generating augmented result sets with integrated quality metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing federated query engines repeatedly pull data segments from disparate remote data sources to resolve complex federated queries, then complete query resolution is achieved, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system performs preliminary actions by receiving and executing quality evaluation queries against data sources before the main federated query is resolved. This allows quality metrics to be pre-computed and cached, so when the actual federated query executes, it can leverage these pre-evaluated quality assessments rather than performing complete data pulls, significantly reducing query resolution time while maintaining reliability
Solution Approach 2:
The system segments the query resolution process into separate components: quality evaluation queries are executed independently from the main federated query. The quality evaluation is divided into distinct tasks that can run in parallel, evaluating different aspects of data quality (completeness, accuracy, timeliness) separately, which reduces the overall time burden on the main query resolution process
2Reliability
If existing federated query engines repeatedly pull data segments from disparate remote data sources, then complete query resolution is achieved, but computing resources are consumed intensively
Solution Approach 1:
The system applies partial action by executing only quality evaluation queries rather than complete data pulls from all data sources. These quality queries are designed to be less resource-intensive partial evaluations that assess data quality without requiring the full data retrieval and processing that would be needed for complete query resolution, thus reducing computing resource consumption while maintaining the ability to resolve queries completely when needed
Solution Approach 2:
Quality evaluations are performed as preliminary actions before main query execution, allowing the system to cache quality metrics and avoid repeated expensive data pulls. This preliminary computation reduces the computing resources needed during actual query resolution, as the system can leverage pre-computed quality assessments instead of performing complete data retrievals
3Measurement precision
If traditional systems monitor data quality separately from query processing, then dedicated quality assessment is achieved, but computing resources are wasted on separate monitoring operations
Solution Approach 1:
The system merges quality evaluation queries with the federated query processing workflow. Instead of running separate monitoring operations, the quality evaluation is integrated into the query execution plan, allowing quality metrics to be computed alongside query results using the same data access infrastructure. This combining eliminates redundant computing operations while maintaining precise quality measurement
Solution Approach 2:
The federated query system is designed to serve multiple functions simultaneously: it performs both the primary query resolution and quality evaluation tasks. The same data access mechanisms and processing infrastructure are used for both purposes, making the system universal and eliminating the need for separate dedicated monitoring resources
4Loss of information
If federated query systems provide data quality metrics, then data contextualization is improved, but the metrics are often inaccurate and outdated
Solution Approach 1:
The system implements feedback mechanisms where quality evaluation results are continuously updated and fed back into the federated query system. As new quality evaluations are performed, the metrics are refreshed and made available for subsequent queries, ensuring that the most current and accurate quality information is always provided with query results, preventing metrics from becoming outdated
Data Source
AI summary
Various embodiments of the present disclosure provide federated query processing techniques for quality evaluation and augmentation of data provided by a federated query system. The techniques include receiving an execution plan for a federated query. The techniques also include receiving one or more data segments from a plurality of third-party data sources and receiving quality evaluation data using a set of data accessing tasks for the execution plan. The techniques also include generating a result set for the federated query using a set of data processing tasks for the execution plan. Additionally, the techniques include generating quality metrics data for the one or more data segments based on the quality evaluation data and generating an augmented result set for the federated query based on the result set and the quality metrics data.