Mainframe Data Adapter for Cloud Database Integration
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Solution Overview
Problem
Mainframe and non-mainframe environments face challenges in data sharing due to differences in data structure and proprietary I/O systems, making it difficult for mainframe resources to interact with non-mainframe resources, particularly in cloud environments, leading to high costs and complexity in data management and migration.
Innovation Solution
A method that monitors mainframe management data in real-time, transforms it into a format readable by non-mainframe databases, and transmits it to a target database, enabling seamless interaction and data sharing between mainframe and non-mainframe environments using metadata transformation and standard protocols like TCP/IP, allowing access to cloud storage systems and other non-mainframe resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mainframe and non-mainframe environments use different data structures and proprietary I/O systems, then each environment maintains its own operational integrity, but data sharing and interaction between environments become difficult and complex
Solution Approach 1:
The patent implements an intermediary system that includes a mainframe adapter, data transformation engine, and target database adapter. This intermediary layer sits between the mainframe environment and non-mainframe environment, translating data structures and protocols. The adapter reads mainframe data in its native format, transforms it to target database format using metadata mappings, and writes to the target database, thereby enabling data sharing without requiring changes to either the mainframe or target systems.
Solution Approach 2:
The patent employs parameter changes by dynamically transforming data structure parameters during the data conversion process. The transformation engine modifies data format parameters, encoding parameters, and structure parameters based on metadata definitions. This allows the same underlying data to be represented in different parameter formats suitable for different environments, resolving the incompatibility between mainframe and non-mainframe data structures.
2Ease of operation
If data is transformed and transmitted between mainframe and non-mainframe environments in real-time, then data accessibility and sharing are improved, but processing time and system resource consumption increase
Solution Approach 1:
The patent implements preliminary action by pre-defining metadata that describes the structure and format of data in both mainframe and target database environments. These metadata definitions are established beforehand, allowing the transformation engine to perform rapid conversions without needing to analyze data structures in real-time. The mapping relationships between source and target data structures are predetermined, reducing processing overhead during actual data transmission.
3Reliability
If proprietary I/O systems are used in mainframe environments, then mainframe operations maintain high performance and reliability, but integration with standard protocols like TCP/IP becomes difficult
Solution Approach 1:
The patent uses an intermediary adapter layer that interfaces with both proprietary mainframe I/O systems and standard TCP/IP networks. The mainframe adapter connects to mainframe I/O channels using proprietary protocols, receives data in mainframe format, transforms it to standard database formats, and transmits via TCP/IP to target databases. This intermediary maintains mainframe performance by using native I/O paths while providing standard protocol compatibility for external systems.
Data Source
AI summary
A method includes monitoring mainframe management data in a mainframe environment in real time and receiving messages of the mainframe management data. A mainframe dataset in the mainframe environment is accessed and metadata defining the structure of data in the mainframe dataset is read. A target database outside of the mainframe environment is identified data in the mainframe dataset is parsed into a series of field names and values. The messages of the mainframe management data are transformed into a format readable by the target database. The data in the mainframe environment is transformed into database data comprising table, column and row identifiers. The transformed messages and the database data are transmitted from the mainframe environment to the target database.