Semantic Multi-Database Data Lake for Heterogeneous Integration
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Solution Overview
Problem
Current data integration techniques face challenges in managing and querying heterogeneous data from different database types, leading to issues like data silos, high development and maintenance costs, data inconsistency, and the identification of 'dark data' in enterprise data lakes.
Innovation Solution
A semantic multi-database data lake architecture that uses ontology definition language commands, specifically Resource Description Framework (RDF), to translate and replicate data across various database types, providing a unified interface for data management and query operations, and automatically detecting dark data by replicating datasets across multiple database engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored separately in different database types, then each database can be optimized for its specific data type, but data silos are created and integration becomes difficult
Solution Approach 1:
The patent introduces an intermediary layer (data lake with unified schema) between heterogeneous databases that translates and integrates data from different database types. This mediator enables data integration while preserving the optimization benefits of specialized databases, resolving the contradiction between type-specific optimization and integration complexity.
Solution Approach 2:
The data lake is designed with universal schema capabilities that can accommodate multiple database types (relational, NoSQL, time-series, graph) through a unified interface. This multi-functional approach allows the system to handle diverse data types without creating silos, maintaining both specialization benefits and integration ease.
2Stability of the object's composition
If multiple database types are integrated in a unified repository, then data consistency is improved, but the complexity of managing different database paradigms increases
Solution Approach 1:
The patent applies homogeneity by enforcing a unified schema across all database types in the data lake. This ensures that data from heterogeneous sources maintains consistent structure and format, improving data consistency while reducing management complexity through standardized handling procedures.
Solution Approach 2:
The unified data lake schema acts as an intermediary that standardizes data representation across different database paradigms. This mediator layer translates diverse data formats into a consistent structure, maintaining data consistency while shielding users from the underlying complexity of multiple database types.
3Productivity
If data is replicated across multiple database engines, then dark data detection is enabled and data accessibility is improved, but storage requirements and maintenance costs increase
Solution Approach 1:
The patent implements selective copying of data across multiple database engines within the unified data lake architecture. This copying strategy enables dark data detection by making data accessible through multiple query interfaces while managing storage requirements through intelligent replication policies that copy only necessary data subsets.
Solution Approach 2:
The unified data lake provides multi-functional access to replicated data, allowing the same data to be queried through different database paradigms (SQL, NoSQL, time-series queries). This universal access capability improves data accessibility and enables dark data detection without proportionally increasing storage requirements, as the replication serves multiple query purposes simultaneously.
4Ease of operation
If a unified interface is provided for heterogeneous databases, then ease of operation is improved, but the complexity of translating between different query languages increases
Solution Approach 1:
The unified data lake schema serves as an intermediary interface that abstracts away the complexity of different database query languages. Users interact with a single standardized interface, while the system handles the translation to appropriate query languages for each underlying database type, improving ease of operation while managing translation complexity internally.
Solution Approach 2:
The patent enforces a homogeneous query interface across all database types in the data lake. This unified interface standardizes data access patterns and query syntax, improving ease of operation while the underlying translation mechanisms handle the heterogeneity of different database paradigms, isolating translation complexity from users.
Data Source
AI summary
Methods and apparatus are provided for integrating a plurality of different database types in a semantic multi-database data lake. An exemplary method comprises providing a plurality of databases having different database types; translating ontology definition language database commands obtained from a user into a plurality of data definition language and/or data manipulation language commands supported by the different database types in order to replicate data from the user to each of the different database types; obtaining a query specified in a query language of a given database; and delegating the query to the given database. A plurality of cluster gateways optionally manage a corresponding plurality of clusters of database instances and wherein queries are delegated to a given database instance by delegating the queries to the appropriate cluster gateway. Dark data that was not queried by any supported query language in a predefined period of time can be detected.