Lakehouse Drift Reconciliation Using Snapshot and CDC Comparison
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Solution Overview
Problem
Conventional methods for detecting and reconciling data drift between transactional and analytical systems, particularly in lakehouses, are inefficient, resource-intensive, and lack the necessary confidence in data quality, leading to inaccurate business decisions and increased risk of errors.
Innovation Solution
A system and method for detecting and reconciling data drift in lakehouses by taking snapshots of transactional databases, reconstructing tables using change data capture, and applying corrective events to ensure data integrity, utilizing lakehouse capabilities for continuous data capture and change data feeds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional drift detection methods are used, then data drift can be detected, but resource consumption increases and manual intervention is required
Solution Approach 1:
The system performs automated drift detection and reconciliation without requiring manual intervention. The drift detection service automatically compares source data with destination data, identifies drift conditions, and the reconciliation service automatically applies corrective events to fix the drift, enabling the system to self-maintain data quality
Solution Approach 2:
The system implements a feedback loop where drift detection continuously monitors data quality, identifies discrepancies between source and destination data, and triggers reconciliation actions to correct the drift. This closed-loop feedback mechanism ensures ongoing maintenance of data integrity without manual intervention
2Measurement precision
If full data comparison is performed for drift detection, then detection accuracy improves, but processing time and resource usage increase
Solution Approach 1:
The system extracts only the necessary data for drift comparison by using change data capture to identify specific records that have changed in the source database. Instead of comparing entire datasets, the system extracts only the changed records and applies the necessary transformations to compare minimal data, significantly reducing processing time while maintaining detection accuracy
Solution Approach 2:
The system performs preliminary actions by pre-defining the set of corrective events that can be applied during reconciliation. Change data capture is used to pre-identify transformations needed, and the reconciliation service has pre-prepared the framework for applying corrective events, allowing rapid drift correction without time-consuming on-the-fly processing
3Reliability
If manual reconciliation processes are used, then data accuracy can be maintained, but operational complexity and error risk increase
Solution Approach 1:
The reconciliation service automatically applies corrective events to fix drift without manual intervention. The system self-manages the complexity of data reconciliation by automatically generating and applying the necessary corrective actions based on drift detection results, eliminating manual operational complexity
Solution Approach 2:
The system introduces an intermediary layer of corrective events that mediates between drift detection and data reconciliation. Instead of directly manual intervention, the system uses automated corrective events as intermediaries to translate drift conditions into automatic reconciliation actions, simplifying the overall process
Data Source
AI summary
Systems and methods are provided for data drift detection and reconciliation by establishing ground truth through the determination of any changes in the source data via restored current and previous snapshots of an operational/transactional database. Changes in lakehouse data can be identified via the use of raw data events from which reconstructed tables are determined, and changes in business intelligence tables can be identified based on the application of data mapping rules to the raw data events, such that these changes can be compared to determine if data drift has occurred.