Multi-model Datastore Enrichment Catalog Provenance Tracking
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Solution Overview
Problem
Current data storage systems lack high-fidelity provenance and lineage, making it difficult for data analysts to accurately track and trust data transformations, leading to opacity and reduced search recall in data analytics.
Innovation Solution
A multi-model datastore system that maintains granular provenance and lineage through a persistent enrichment memory graph and catalog, allowing for uniform representation and improved search recall by registering and querying enrichment functions and their associations with raw data values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data transformations are tracked with detailed provenance and lineage, then search recall and data trust are improved, but device complexity and computational overhead increase
Solution Approach 1:
The system segments data transformations into discrete enrichment functions that can be individually tracked and managed. Each enrichment function operates as an independent unit that transforms input data to output data, with provenance metadata captured at each step. This segmentation allows the complex tracking system to be broken down into manageable, composable units that can be executed and tracked separately.
Solution Approach 2:
The system introduces an intermediary enrichment layer between raw data sources and final data consumers. This intermediary layer captures provenance metadata and maintains lineage information without requiring changes to the underlying data sources or consumer systems. The enrichment functions act as mediators that add tracking capability while preserving the existing data ecosystem.
2Loss of information
If detailed provenance and lineage are maintained for all data transformations, then search recall is improved, but computational overhead and processing time increase
Solution Approach 1:
The system performs preliminary enrichment of data with provenance metadata at the point of transformation, rather than computing lineage information on-demand during queries. By capturing enrichment metadata upfront during data processing, the system avoids repeated computational overhead during search operations, as the provenance information is already structured and stored for efficient retrieval.
Solution Approach 2:
The system creates lightweight copies of enrichment metadata alongside the transformed data, storing provenance information in a compact format that can be quickly accessed during searches. Instead of reconstructing entire lineage graphs during queries, the system uses pre-copied metadata records that contain essential provenance details, significantly reducing query processing time.
3Adaptability or versatility
If enrichment functions are executed on raw data values, then data enrichment capability is improved, but computational resources and processing overhead increase
Solution Approach 1:
The system changes the parameter of data representation by enriching raw data values with additional attributes and metadata through executable enrichment functions. These functions transform data from a basic state to an enriched state with enhanced properties, allowing the same data to serve multiple analytical purposes without requiring separate processing pipelines for each enrichment type.
Solution Approach 2:
The system implements universal enrichment functions that can operate on multiple types of raw data values using the same underlying mechanism. Rather than implementing separate enrichment logic for each data type or transformation scenario, the system uses a unified enrichment framework that adapts to different data sources and transformation requirements, reducing overall computational overhead through code reuse and standardized processing.
Data Source
AI summary
A multi-model data store comprises an enrichment catalog and an enrichment memory graph. Raw data from a source raw datastore is enriched based on an enrichment function. Enriched data is stored in graph nodes with edge associations indicating the enrichment type and confidence level. The enriched data is also stored in the enrichment catalog with full breadcrumb provenance and lineage identifying, with cataloged enrichment steps, down to a record and field in the source raw datastore. Additional enriched values and associations between graph nodes can be entered in the graph by a user. Queries to the multi-model database begin in the graph and continue to the enrichment catalog. Query results include the full sequential chain of enrichments to source raw data records and fields, and may include dynamically generated virtual enrichment catalog records that link search values not found in the enrichment catalog to enrichment catalog records via the graph.