Data Lake Deduplication via Change Log Segmentation
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Solution Overview
Problem
Data lakes face challenges with inconsistent and duplicate data entries, leading to incorrect results in downstream data analytics due to the lack of mechanisms to identify and deduplicate valid records, which conventional techniques cannot effectively address without compromising performance and scalability.
Innovation Solution
A transformation service is implemented to efficiently scan change logs, identify updated partitions, and deduplicate records based on reliable criteria, ensuring only the latest and valid data is processed, thereby maintaining performance and scalability without additional overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional techniques are used to store and process data in data lakes, then data storage capacity is improved, but data consistency and accuracy deteriorate due to duplicate and inconsistent entries
Solution Approach 1:
The system performs preliminary deduplication and validation actions during data ingestion into the data lake. Change tracking mechanisms are established beforehand to monitor and identify duplicate records before they cause downstream processing issues, ensuring data consistency is maintained from the point of entry.
Solution Approach 2:
An intermediary layer is introduced between data ingestion and downstream processing that includes change tracking and deduplication logic. This intermediary service validates records, identifies duplicates based on defined criteria, and ensures only consistent, accurate data proceeds to downstream analytics, resolving the contradiction between storage capacity and data reliability.
2Productivity
If data is periodically retrieved and transformed from data lake, then data processing capability is improved, but analysis time increases due to lack of change detection mechanisms
Solution Approach 1:
Change tracking is performed preliminarily during data ingestion, creating a record of what has changed before downstream processing occurs. This preliminary change detection eliminates the need for downstream systems to perform time-consuming full data scans, reducing analysis time while maintaining processing capability.
Solution Approach 2:
The system extracts only the changed portions of data from the data lake using change tracking information, rather than retrieving and transforming entire datasets periodically. This extraction of minimal necessary data maintains processing capability while significantly reducing analysis time by avoiding unnecessary data movement and transformation.
3Measurement precision
If change logs are scanned to identify updated partitions, then data accuracy is improved, but system performance deteriorates due to fast-growing log tables requiring full table scans
Solution Approach 1:
The change tracking system segments the large change log into smaller, manageable partitions or time-based segments. This segmentation allows the scanning process to operate on smaller subsets of data rather than performing full table scans on growing log tables, maintaining change detection accuracy while improving scan performance through reduced data access volume.
Solution Approach 2:
The system implements periodic scanning of change logs at scheduled intervals rather than continuous scanning. Change tracking information is accumulated and then processed in periodic batches, which maintains accurate change detection while optimizing performance by avoiding constant full-table scan operations and allowing for more efficient batch processing.
Data Source
AI summary
A request may be received to transform records in a data lake that match one or more query criteria. Data lake records that match the criteria may be retrieved. The retrieved records may include two data lake records associated with the same data lake record identifier. A transformed record may be generated based on time stamps associated with the retrieved records.