Object Version Tracking for Read-Time Data Federation
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Solution Overview
Problem
Conventional data analysis platforms face challenges in handling large volumes of data from disparate sources due to storage capacity limitations and inefficiencies in data federation, leading to unnecessary delays and resource constraints.
Innovation Solution
A system that utilizes adapter nodes to retrieve and join data fragments from different data stores, transforming them into a canonical view of an object using smart versioning caches and APIs, allowing for efficient read/write-time data access and reducing storage requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If all data from disparate data sources is downloaded or ingested onto the platform to enable data federation, then users can retrieve data from multiple sources with a single query, but the storage capacity of the platform is exceeded when the amount of data is large
Solution Approach 1:
The patent segments the data federation architecture into multiple adapter nodes, each responsible for specific data sources. Instead of centralizing all data in one platform database, the system divides data access responsibilities across distributed adapter nodes that query original data sources directly, eliminating the need to ingest all data onto the platform while maintaining federation capabilities.
Solution Approach 2:
The patent introduces adapter nodes as intermediary components between the data analysis platform and disparate data sources. These adapter nodes act as mediators that translate platform queries into source-specific queries, retrieve data fragments, and return results without requiring data to be physically transferred to the platform, thus enabling federation without exceeding storage capacity.
2Productivity
If data is ingested onto the platform to enable analysis, then data access and manipulation can be performed efficiently, but unnecessary delays and resource constraints occur when dealing with large volumes of data
Solution Approach 1:
The patent implements preliminary actions by pre-configuring adapter nodes with knowledge of data source schemas, query translation rules, and data fragment locations. This pre-preparation allows adapter nodes to immediately execute queries without requiring time-consuming data ingestion or schema discovery during runtime, thus maintaining processing efficiency while avoiding ingestion delays.
Solution Approach 2:
The patent extracts only the necessary data fragments from data sources that are required for specific analysis tasks, rather than ingesting all data. The adapter nodes query for specific objects or data subsets based on user requests, retrieve only relevant fragments, and return them for analysis, eliminating unnecessary data transfer time and resource consumption.
3Ease of operation
If data fragments are retrieved from multiple data stores and joined to create a canonical view, then a unified object view can be provided to applications, but additional complexity is introduced in data retrieval and joining operations
Solution Approach 1:
The patent creates a universal adapter node architecture where each adapter can handle multiple data sources and implement consistent query translation, data fragment retrieval, and joining logic. This multi-functional design allows the same adapter patterns to be applied across different data sources, simplifying the overall complexity by providing a unified approach rather than requiring source-specific custom logic for each data retrieval operation.
Solution Approach 2:
The patent uses copying by creating standardized adapter templates and query translation patterns that can be replicated across different data sources. Instead of developing complex custom retrieval logic for each data source, the system copies and adapts proven adapter patterns, reducing the complexity of implementing unified data access across disparate sources while maintaining ease of operation.
Data Source
AI summary
Systems and methods are provided in order to avoid ingesting the entire contents of one or more data sources. An object may be associated with one or more pieces/fragments of data that can be stored in different data sources. When a request for the object is initiated, a search for those pieces/fragments of data can be performed. Nodes associated with each data source may generate data queries appropriate for its corresponding data source, retrieve, and if needed transform the data into an object-based data structure. Any pieces/fragments of data that have been discovered and retrieved can be joined, e.g., by an application programming interface server, and forwarded to a requesting client or application. In this way, only data relevant to the object is obtained. Moreover, object versioning can be employed so that the most up-to-date data is obtained.