One-Click ETL Data Migration for SAP S/4HANA
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Solution Overview
Problem
Existing SAP customers face challenges in migrating data from legacy systems to SAP S/4HANA due to complex dependencies, extensive data volumes, and high costs, making it difficult to justify the business case for transformation projects, especially with the brownfield approach, which is technically complicated and resource-intensive, while the greenfield approach risks devaluing custom investments.
Innovation Solution
A one-click transformation method that separates historical and operational data, allowing for efficient and automated data filtering, cleaning, and transfer to a separate platform, enabling flexible data selection and optimization, and reducing the data load on the new system, thereby simplifying the migration process and lowering operational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the brownfield approach is used for data migration, then existing business processes and data are preserved, but the migration becomes technically complicated and resource-intensive
Solution Approach 1:
The patent segments the data migration process into distinct phases: extraction of source data, separation of operational and historical data, transformation of operational data, and loading into target system. This segmentation allows complex migration to be managed through standardized, reusable components that can be executed automatically, reducing overall complexity while preserving existing data and processes.
Solution Approach 2:
The patent introduces an intermediary data transformation layer that sits between the source and target systems. This intermediary layer handles the complex transformation logic, data cleaning, and validation, allowing the migration process to preserve existing business processes while managing complexity through a standardized intermediate representation.
2Loss of information
If all source data is migrated to the target environment, then complete data availability is achieved, but the data volume and transformation costs increase significantly
Solution Approach 1:
The patent extracts only the necessary operational data from the source system for migration to the target environment, while separating historical data that can be archived or accessed through alternative means. This extraction principle reduces the data volume requiring transformation and migration while maintaining availability of essential business data.
Solution Approach 2:
The patent applies different quality and transformation rules to different types of data based on their specific requirements. Operational data receives full transformation and validation, while historical data receives simplified processing or is archived with minimal transformation, optimizing resource usage while maintaining data availability where needed.
3Manufacturing precision
If data filtering and transformation is performed manually, then data quality can be ensured, but the processing time and resources increase
Solution Approach 1:
The patent implements self-service data transformation through automated ETL processes that perform extraction, transformation, and loading without manual intervention. The system automatically validates data quality, applies transformation rules, and handles errors, ensuring consistent data quality while dramatically reducing processing time compared to manual methods.
Solution Approach 2:
The patent uses parameter-driven transformation rules that can be configured and adjusted without changing the underlying process logic. This allows data quality requirements to be enforced through configurable parameters and rules that automate the filtering and transformation process, maintaining precision while reducing manual processing time.
Data Source
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AI summary
A computer implemented method for processing information related to an extract-transform-load (ETL) data migration, may include extracting a full set of transactional data, master data and customizing data from a source system, separating said transactional data into history data and operational data, creating a copy of said source system without said transactional data, creating an instance of a target system by performing a combined system conversion and database migration on said copy of said source system, transforming said operational data in such a way that it becomes compatible with a data schema of a database for operational data on said target system, loading said transformed operational data into said database for operational data, loading said history data into a database for history data on an archive system, and loading a search help module in relation to a user query for transactional data on said target system.