Transactional Database Real-Time Data Feed Transformation
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Solution Overview
Problem
Existing database systems face challenges in real-time transformation of data feeds from sources like sensor readings and financial market tick data for archival storage, as they require different tools for transaction processing and long-term archiving, necessitating a solution for seamless integration without external systems.
Innovation Solution
A real-time transactional database management system that receives and transforms data feeds into a format suitable for archival storage within the same system, ensuring ACID compliance, fault tolerance, and continuous processing without external intermediaries, using a combination of techniques like serial transaction accumulation, distributed leader election, and user-extensible plugins for efficient extraction and loading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate external systems are used for transforming data feeds for archival storage, then transformation capability is provided, but system complexity and integration requirements increase
Solution Approach 1:
The patent combines the transformation capability directly within the transactional database management system by implementing transformation rules and functions that operate on data feeds during normal transaction processing. This merging eliminates the need for separate external transformation systems while maintaining the required adaptability for different data formats and archival requirements.
Solution Approach 2:
The transactional database management system is enhanced with multi-functionality to perform both traditional transaction processing and data transformation for archival purposes. The system universally handles various data feed formats from multiple sources (sensors, financial markets, logistics) and transforms them into appropriate archival formats, eliminating the need for specialized external transformation systems.
2Productivity
If real-time transformation is performed within the same system, then processing efficiency improves, but system resource demands increase
Solution Approach 1:
The transformation process operates continuously alongside transaction processing without interruption or batch operations. Transformation rules are applied in real-time as data feeds arrive, maintaining continuous useful action that improves processing efficiency while avoiding the resource spikes associated with batch transformations.
Solution Approach 2:
The system applies transformation only to the specific portions of data feeds that require archival storage, rather than transforming entire data sets or over-transforming data. This partial action approach maintains processing efficiency while optimizing resource utilization by avoiding unnecessary transformation operations.
3Reliability
If data feeds are stored in source database and transformed in real-time, then data availability is maintained, but storage requirements increase
Solution Approach 1:
The system segments data storage into two distinct portions: the source database retains original data feeds for data availability and query operations, while the archival database stores transformed data for long-term retention and historical analysis. This segmentation maintains data availability while optimizing storage requirements by storing only necessary copies in each location.
Solution Approach 2:
Different storage locations are assigned different data qualities and formats optimized for their specific purposes. The source database maintains high-availability original formats for active transactions, while the archival database stores compressed or formatted data optimized for long-term retention. This local quality optimization reduces overall storage requirements while maintaining data availability where needed.
Data Source
AI summary
Methods and systems are disclosed for real-time transactional database transformation implemented as part of a real-time transactional database management system.