Migration Tool for Analytics Logic Extraction
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Solution Overview
Problem
Migrating legacy business intelligence tools from on-premise deployments to cloud environments is labor-intensive and time-consuming due to the need to recreate analytics logic and migrate data, often requiring collaboration between IT and business users, and often involves unnecessary data replication.
Innovation Solution
A migration tool that accesses source metadata to identify relevant database artifacts, uses a trained machine learning model to derive data relationships, and generates analytics reports based on user-selected relationships, streamlining the migration process and improving report quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional migration methods are used to migrate legacy BI tools from on-premise to cloud, then data can be transferred, but the process becomes labor-intensive and time-consuming due to manual recreation of analytics logic
Solution Approach 1:
The system automatically copies and transfers analytics logic from the source on-premise environment to the target cloud environment using machine learning models. Instead of manually recreating analytics logic, the system extracts logic from source metadata and database artifacts, then replicates it in the target environment, dramatically reducing migration time and labor requirements.
Solution Approach 2:
The migration system performs self-service by automatically identifying, extracting, and transferring analytics logic without requiring manual intervention from IT or business users. The machine learning model autonomously analyzes source metadata, identifies relevant database artifacts, and reconstructs analytics logic in the target environment, eliminating the need for collaborative manual recreation between different user groups.
2Reliability
If all data is replicated during migration from source to target deployment, then complete data transfer is achieved, but unnecessary data replication increases migration complexity and time
Solution Approach 1:
The system extracts only the necessary data and analytics logic from the source environment by analyzing source metadata and identifying relevant database artifacts. Instead of replicating all data, the machine learning model selectively extracts data that is actually used in analytics reports, eliminating unnecessary data replication and reducing migration complexity while maintaining data completeness for relevant information.
Solution Approach 2:
The system performs partial data replication by focusing only on the subset of data that is relevant to analytics logic. Rather than copying entire databases, the machine learning model identifies and transfers only the portions of data that are actually utilized in business intelligence reports, reducing the scope of migration work while ensuring all necessary data is preserved.
3Manufacturing precision
If manual collaboration between IT and business users is required to rebuild analytics logic, then report accuracy can be maintained, but the migration process becomes excessively time-consuming
Solution Approach 1:
The system replaces the mechanical process of manual collaboration between IT and business users with an automated machine learning-based system. The machine learning model automatically analyzes source metadata, identifies database artifacts, and reconstructs analytics logic without requiring human users to manually recreate reports. This substitution maintains report accuracy through intelligent algorithmic analysis while eliminating the time-consuming collaborative recreation process.
Solution Approach 2:
The machine learning model acts as an intermediary between the source and target environments, automatically translating and adapting analytics logic. Instead of requiring direct human collaboration to bridge the gap between source and target systems, the machine learning intermediary autonomously performs the translation, maintaining accuracy while eliminating the need for manual user involvement in the recreation process.
Data Source
AI summary
Various examples are directed to systems and methods for migrating an analytics tool from a source deployment to a target deployment. A migration tool may access source metadata describing a plurality of source deployment queries to the at least one source deployment database. The migration tool may identify a plurality of source deployment database artifacts using the source metadata and migrate the plurality of source deployment database artifacts to a target database. The migration tool may apply a trained model to the plurality of source deployment database artifacts to generate a set of data relationships and generate a first analytics report from the migrated plurality of source deployment database artifacts at the target database, the first analytics report corresponding to at least one of the set of data arrangements.