ETL Query Parser Tree Structure for Multi-Tenant Data Extraction
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Solution Overview
Problem
Current ETL systems lack a scalable framework for working in multi-tenant federated setups with diverse source schematic representations, making it difficult to form OLAP cubes efficiently.
Innovation Solution
An ETL system is developed that includes a data query module for defining input queries, a query parsing module to create a tree structure of distinct queries, and a data extraction module to extract and copy transactional data across multiple source machines with different schematic representations to a destination system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current ETL systems are used to extract data from multi-tenant federated sources with diverse schematic representations, then data extraction can be performed, but the system lacks scalability and cannot efficiently form OLAP cubes
Solution Approach 1:
The patent segments the ETL system into distinct modular components: a query parser that handles diverse schemas, a federation manager that coordinates multiple tenant databases, and an OLAP cube generator that processes extracted data. This modular architecture allows each component to independently handle specific aspects of data extraction from heterogeneous sources, improving adaptability while managing complexity through clear separation of concerns.
Solution Approach 2:
The patent implements a universal ETL framework that can handle multiple types of data sources (relational databases, NoSQL databases, flat files) and various schematic representations through a common interface. The federation manager provides multi-tenant support that works across different organizational units, and the query parser handles diverse SQL dialects and schema formats, enabling a single system to serve multiple functions and data types.
2Quantity of substance
If data is collected from multiple channels and stored in different representations across different machines, then data volume and source diversity increase, but it becomes difficult to manage and integrate the data efficiently
Solution Approach 1:
The patent introduces a federation manager as an intermediary layer between the diverse data sources and the ETL processing components. This mediator translates queries from a unified schema into source-specific queries, handles data extraction from multiple channels, and manages tenant-specific data access. The query parser acts as another intermediary that standardizes diverse query formats into a common internal representation, making data integration easier despite the volume and diversity of sources.
Solution Approach 2:
The patent creates a virtual copy of the federated data structure in memory through the federation manager, allowing the system to work with a standardized representation of multi-tenant data without physically duplicating the underlying diverse data sources. This virtual copying enables efficient data manipulation and OLAP cube formation while maintaining the integrity of the original distributed data architecture.
Data Source
AI summary
An ETL system for extracting transactional data from a plurality of source machines is provided. The ETL system includes a data query module configured to provide an interface to enable a data analyst to define an input query to extract transactional data, which is present on a plurality of source machines. The source machines form a multi-tenant system and each source machine is identified by a unique tenant identifier. The system also includes a query parsing module coupled to the data query module and configured to deconstruct the input query to create a tree structure. The tree structure comprises a plurality of distinct queries. The system further includes a data extraction module coupled to the query parsing module and configured to extract transactional data based on the distinct queries and copy the transactional data to a destination system wherein the transactional data comprises a plurality of data types.