Data Warehouse Workload Transformation via Canonical Model Optimization
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Solution Overview
Problem
Conventional systems face limitations in converting ETL jobs from source data warehouses to target systems, including compatibility issues with modern databases, complex translation processes, and introduction of redundancies, leading to time-consuming and difficult data migration processes.
Innovation Solution
A system and method for data warehouse workload transformation that includes a processing subsystem with modules for data extraction, canonical model generation, optimization, and code generation, which receives job metadata, extracts domain logic, creates relational models, optimizes query execution plans, and maps entities to target environments, generating optimized code for the target environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional systems convert ETL mappings to ELT scripts, then compatibility with modern databases is improved, but the translation process becomes more complex and time-consuming
Solution Approach 1:
The patent uses template-based copying where predefined templates are instantiated and filled with extracted metadata values to generate target ETL mappings. This avoids complex translation while maintaining compatibility, as templates are pre-validated for target database compatibility.
Solution Approach 2:
The system extracts parameters from source ETL mappings and transforms them into target-specific parameter formats through structured conversion. Metadata values are parameterized and substituted into templates, enabling efficient parameter transformation without complex logic translation.
2Productivity
If conventional systems convert ETL mappings, then data migration is achieved, but redundancies are introduced during translation
Solution Approach 1:
The patent extracts only essential metadata values from source ETL mappings (such as table names, column names, data types) and uses these extracted values to populate target templates. This extraction approach eliminates redundant translation logic while preserving only the necessary data migration information.
Solution Approach 2:
Multiple redundant translation steps are merged into a single template instantiation operation. The system combines extraction, transformation, and validation into one unified process where metadata is extracted and directly instantiated into target mappings, eliminating intermediate redundant steps.
3Measurement precision
If conventional systems require prerequisite information about ETL tools, then translation accuracy is improved, but device complexity increases
Solution Approach 1:
Instead of requiring complex analysis of ETL tool-specific syntax, the system uses standardized templates that copy proven target database patterns. This template-based copying provides translation accuracy without requiring the system to understand or store complex tool-specific prerequisite information.
Solution Approach 2:
The patent implements a universal template framework that can generate mappings for multiple target databases using a single set of standardized templates. This multi-functional approach eliminates the need for separate translation logic for different ETL tools, reducing system complexity while maintaining accuracy.
4Adaptability or versatility
If conventional systems rewrite mappings during migration, then adaptability to target systems is improved, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-creating and pre-validating target database templates before migration occurs. These templates are prepared in advance with all necessary target-specific logic and syntax, so during migration only simple parameter substitution is needed, eliminating time-consuming rewrite operations.
Solution Approach 2:
The patent uses template copying where pre-validated target templates are copied and instantiated with source metadata values. This copying approach provides rapid adaptation to target systems without requiring time-consuming rewrites, as the templates already embody the necessary adaptability.
Data Source
AI summary
A system for data warehouse workload transformation is disclosed. A data extraction module receives job metadata from one or more extract transform load applications, extracts domain logic information from the job metadata. A canonical model generation module creates one or more relational models from the domain logic information, generates a canonical model for the job metadata by merging the one or more relational models. A model optimization module creates a query execution plan using an optimization technique, passes the canonical model through one or more optimization layers, and optimizes the query execution plan to execute common logic at a single point of time for removing one or more redundant operations. A code generation module generates an output code corresponding to the predefined target environment from an optimized canonical model, upon mapping of the one or more entities, for the workload transformation.