Runtime Impact Analysis Module for ETL Job Identification
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Solution Overview
Problem
Existing ETL software tools lack the functionality to identify ETL jobs that are using a particular data repository during runtime, making it impractical to manually review information about each job to determine repository usage.
Innovation Solution
A system that allows users to specify a data repository, queries ETL servers for a list of jobs, retrieves runtime information, and identifies ETL jobs using the repository by searching for the repository's designator within the runtime data, providing a list of impacted jobs to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of each ETL job information is performed to determine repository usage, then complete information about job usage can be obtained, but significant time and effort are required
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated computer-based system that queries ETL servers, retrieves runtime information, and automatically identifies ETL jobs using the specified data repository. This substitution eliminates the time-consuming manual effort while maintaining complete and accurate information about repository usage.
2Ease of operation
If existing ETL software tools are used without runtime impact analysis functionality, then the system remains simple, but the ability to identify jobs using a particular data repository is lost
Solution Approach 1:
The patent introduces an intermediary runtime impact analysis module that sits between the user and the ETL jobs. This module automatically queries the ETL servers, retrieves runtime information, and presents the repository usage information to users without requiring changes to the underlying ETL tool complexity. Users can specify a data repository and receive automated identification of impacted jobs, gaining visibility without adding operational complexity.
3Productivity
If automated querying and analysis of runtime information is implemented, then identification of impacted ETL jobs is efficient and accurate, but system complexity increases
Solution Approach 1:
The patent segments the runtime impact analysis functionality into distinct modular components: a runtime impact analysis module that manages the overall process, query components that interact with ETL servers, analysis components that process runtime information, and output components that present results. This segmentation allows the system to achieve high productivity in identifying impacted jobs while keeping each component relatively simple and maintainable.
Data Source
AI summary
A system for analyzing the runtime impact of data files on data extraction, transformation, and loading (ETL) jobs typically includes a processor, a memory, and a runtime impact analysis module stored in the memory. The runtime impact analysis module is typically configured for: receiving a user input comprising a designator of a data repository from a user; receiving an ETL job list file from one or more ETL servers; for each ETL job invocation name in the ETL job list file: querying the one or more ETL servers for runtime information, receiving the runtime information from the one or more ETL servers, searching the runtime information for the designator of the data repository, and if the designator of the data repository is identified in the runtime information, adding the ETL job invocation name to an impacted ETL job file; and providing the impacted ETL job file to the user.